2019 Systematic review Panoramic Alveolar Nerve Damage
Packages
library(tidyverse)Registered S3 method overwritten by 'dplyr':
method from
print.rowwise_df
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[31mā[30m [34mdplyr[30m::[32mlag()[30m masks [34mstats[30m::lag()[39m# install.packages("mada")
# install.packages("meta")
library(mada)Loading required package: mvtnorm
Loading required package: ellipse
Attaching package: āellipseā
The following object is masked from āpackage:graphicsā:
pairs
Loading required package: mvmeta
This is mvmeta 0.4.11. For an overview type: help('mvmeta-package').
Attaching package: āmadaā
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speclibrary(meta)Loading 'meta' package (version 4.9-5).
Type 'help(meta)' for a brief overview.
Attaching package: āmetaā
The following object is masked from āpackage:madaā:
forestDataset
# install.packages("rapportools")
df$SIGNO <- rapportools::tocamel(df$SIGNO)
df <- df %>%
mutate(SIGNO = case_when(
SIGNO == "InterrupciĆ³nDeLaCorticalCanalMandibular" ~ "InterrupciĆ³nDeCorticalCanalMandibular",
SIGNO == "OscurecimientoDeLaRaiz" ~ "OscurecimientoDeLasRaices",
TRUE ~ as.character(SIGNO)
))df <- janitor::clean_names(df)aNumero <- function(x, na.rm = FALSE) (as.integer(x))
df <- df %>%
mutate_at(c("a_seleccion_pacientes",
"a_referencia_estandar",
"especificidad"), aNumero)EDA
table(df$autor)
A. HASANI A. KADAM A.JHAMB H. GHAEMINIA L. H. MATZEN S. KURSUN TANTANAPORNKUL
12 4 12 10 6 5 8 Calidad
df %>%
mutate(riesgoDeSesgo = rs_seleccion_paciente + rs_test_indice + rs_referencia_estandar) %>%
mutate(aplicabilidad = a_flujo_y_tiempo + a_seleccion_pacientes + a_test_indice + a_referencia_estandar) %>%
group_by(autor) %>%
summarise(sesgo = mean(riesgoDeSesgo), aplicab = mean(aplicabilidad))Meta-analysis
table(df$signo)
DesviaciĆ³nDeLasRaices DesviaciĆ³nDelCanalMandibular DilaceraciĆ³nDeRaices
5 12 1
EstrechamientoDeLasRaices EstrechamientoDelCanalMandibular InterrupciĆ³nDeCorticalCanalMandibular
4 5 12
OscurecimientoDeLasRaices OscurecimientoYDobleContornoDeRaices SuperposiciĆ³nRaicesEnCanalMandibular
10 5 3 df %>%
mutate_if(is.character, funs(factor(trimws(.)))) %>%
group_by(signo) %>%
summarise(n = n())Creo los totales
df <- df %>%
mutate(n1 = vp + fp,
n2 = vn + fn)md1 <- df %>%
select(autor,
TP = vp,
FP = fp,
TN = vn,
FN = fn) %>%
na.omit() %>%
madad(., level = 0.95)Unknown or uninitialised column: 'names'.print(md1, digits = 2)Descriptive summary of . with 42 primary studies.
Confidence level for all calculations set to 95 %
Using a continuity correction of 0.5 if applicable
Diagnostic accuracies
sens 2.5% 97.5% spec 2.5% 97.5%
[1,] 0.98 0.83 1.00 0.05 0.01 0.19
[2,] 0.98 0.83 1.00 0.05 0.01 0.19
[3,] 0.98 0.83 1.00 0.05 0.01 0.19
[4,] 0.98 0.83 1.00 0.05 0.01 0.19
[5,] 0.98 0.83 1.00 0.05 0.01 0.19
[6,] 0.94 0.77 0.99 0.24 0.13 0.42
[7,] 0.94 0.77 0.99 0.24 0.13 0.42
[8,] 0.94 0.77 0.99 0.24 0.13 0.42
[9,] 0.94 0.77 0.99 0.24 0.13 0.42
[10,] 0.94 0.77 0.99 0.24 0.13 0.42
[11,] 0.70 0.51 0.83 0.73 0.64 0.81
[12,] 0.70 0.51 0.83 0.73 0.64 0.81
[13,] 0.70 0.51 0.83 0.73 0.64 0.81
[14,] 0.70 0.51 0.83 0.73 0.64 0.81
[15,] 0.91 0.75 0.97 0.67 0.59 0.75
[16,] 0.91 0.75 0.97 0.67 0.59 0.75
[17,] 0.91 0.75 0.97 0.67 0.59 0.75
[18,] 0.91 0.75 0.97 0.67 0.59 0.75
[19,] 0.96 0.86 0.99 0.02 0.00 0.20
[20,] 0.96 0.86 0.99 0.02 0.00 0.20
[21,] 0.96 0.86 0.99 0.02 0.00 0.20
[22,] 0.96 0.86 0.99 0.02 0.00 0.20
[23,] 0.96 0.86 0.99 0.02 0.00 0.20
[24,] 0.96 0.86 0.99 0.02 0.00 0.20
[25,] 0.92 0.79 0.97 0.92 0.73 0.98
[26,] 0.92 0.79 0.97 0.92 0.73 0.98
[27,] 0.92 0.79 0.97 0.92 0.73 0.98
[28,] 0.92 0.79 0.97 0.92 0.73 0.98
[29,] 0.92 0.79 0.97 0.92 0.73 0.98
[30,] 0.92 0.79 0.97 0.92 0.73 0.98
[31,] 0.95 0.66 0.99 0.54 0.35 0.73
[32,] 0.95 0.66 0.99 0.54 0.35 0.73
[33,] 0.95 0.66 0.99 0.54 0.35 0.73
[34,] 0.95 0.66 0.99 0.54 0.35 0.73
[35,] 0.95 0.66 0.99 0.54 0.35 0.73
[36,] 0.95 0.66 0.99 0.54 0.35 0.73
[37,] 0.85 0.54 0.96 0.93 0.76 0.98
[38,] 0.85 0.54 0.96 0.93 0.76 0.98
[39,] 0.85 0.54 0.96 0.93 0.76 0.98
[40,] 0.85 0.54 0.96 0.93 0.76 0.98
[41,] 0.85 0.54 0.96 0.93 0.76 0.98
[42,] 0.85 0.54 0.96 0.93 0.76 0.98
Test for equality of sensitivities:
X-squared = 84.9717, df = 41, p-value = 6.6e-05
Test for equality of specificities:
X-squared = 590.0949, df = 41, p-value = <2e-16
Diagnostic OR and likelihood ratios
DOR 2.5% 97.5% posLR 2.5% 97.5% negLR 2.5% 97.5%
[1,] 2.39 0.09 61.40 1.03 0.93 1.14 0.43 0.02 10.11
[2,] 2.39 0.09 61.40 1.03 0.93 1.14 0.43 0.02 10.11
[3,] 2.39 0.09 61.40 1.03 0.93 1.14 0.43 0.02 10.11
[4,] 2.39 0.09 61.40 1.03 0.93 1.14 0.43 0.02 10.11
[5,] 2.39 0.09 61.40 1.03 0.93 1.14 0.43 0.02 10.11
[6,] 4.79 0.76 30.32 1.24 0.99 1.55 0.26 0.05 1.37
[7,] 4.79 0.76 30.32 1.24 0.99 1.55 0.26 0.05 1.37
[8,] 4.79 0.76 30.32 1.24 0.99 1.55 0.26 0.05 1.37
[9,] 4.79 0.76 30.32 1.24 0.99 1.55 0.26 0.05 1.37
[10,] 4.79 0.76 30.32 1.24 0.99 1.55 0.26 0.05 1.37
[11,] 6.28 2.50 15.75 2.60 1.73 3.91 0.41 0.23 0.74
[12,] 6.28 2.50 15.75 2.60 1.73 3.91 0.41 0.23 0.74
[13,] 6.28 2.50 15.75 2.60 1.73 3.91 0.41 0.23 0.74
[14,] 6.28 2.50 15.75 2.60 1.73 3.91 0.41 0.23 0.74
[15,] 20.99 5.45 80.83 2.78 2.13 3.65 0.13 0.04 0.44
[16,] 20.99 5.45 80.83 2.78 2.13 3.65 0.13 0.04 0.44
[17,] 20.99 5.45 80.83 2.78 2.13 3.65 0.13 0.04 0.44
[18,] 20.99 5.45 80.83 2.78 2.13 3.65 0.13 0.04 0.44
[19,] 0.69 0.03 17.78 0.99 0.90 1.08 1.43 0.06 33.54
[20,] 0.69 0.03 17.78 0.99 0.90 1.08 1.43 0.06 33.54
[21,] 0.69 0.03 17.78 0.99 0.90 1.08 1.43 0.06 33.54
[22,] 0.69 0.03 17.78 0.99 0.90 1.08 1.43 0.06 33.54
[23,] 0.69 0.03 17.78 0.99 0.90 1.08 1.43 0.06 33.54
[24,] 0.69 0.03 17.78 0.99 0.90 1.08 1.43 0.06 33.54
[25,] 135.67 18.52 993.94 12.22 2.62 57.12 0.09 0.03 0.25
[26,] 135.67 18.52 993.94 12.22 2.62 57.12 0.09 0.03 0.25
[27,] 135.67 18.52 993.94 12.22 2.62 57.12 0.09 0.03 0.25
[28,] 135.67 18.52 993.94 12.22 2.62 57.12 0.09 0.03 0.25
[29,] 135.67 18.52 993.94 12.22 2.62 57.12 0.09 0.03 0.25
[30,] 135.67 18.52 993.94 12.22 2.62 57.12 0.09 0.03 0.25
[31,] 22.62 1.17 436.42 2.08 1.30 3.32 0.09 0.01 1.41
[32,] 22.62 1.17 436.42 2.08 1.30 3.32 0.09 0.01 1.41
[33,] 22.62 1.17 436.42 2.08 1.30 3.32 0.09 0.01 1.41
[34,] 22.62 1.17 436.42 2.08 1.30 3.32 0.09 0.01 1.41
[35,] 22.62 1.17 436.42 2.08 1.30 3.32 0.09 0.01 1.41
[36,] 22.62 1.17 436.42 2.08 1.30 3.32 0.09 0.01 1.41
[37,] 81.22 7.38 893.94 13.03 2.71 62.58 0.16 0.04 0.70
[38,] 81.22 7.38 893.94 13.03 2.71 62.58 0.16 0.04 0.70
[39,] 81.22 7.38 893.94 13.03 2.71 62.58 0.16 0.04 0.70
[40,] 81.22 7.38 893.94 13.03 2.71 62.58 0.16 0.04 0.70
[41,] 81.22 7.38 893.94 13.03 2.71 62.58 0.16 0.04 0.70
[42,] 81.22 7.38 893.94 13.03 2.71 62.58 0.16 0.04 0.70
Correlation of sensitivities and false positive rates:
rho 2.5 % 97.5 %
0.58 0.33 0.75 oldpar <- par(mfrow=c(1, 2), pty="m")DesviaciĆ³n raĆces / clinica
md1 <- df %>%
filter(signo == "DesviaciĆ³nDeLasRaices") %>%
select(autor,
TP = vp,
FP = fp,
TN = vn,
FN = fn) %>%
na.omit() %>%
madad(., level = 0.95)Unknown or uninitialised column: 'names'.md1 Descriptive summary of . with 5 primary studies.
Confidence level for all calculations set to 95 %
Using a continuity correction of 0.5 if applicable
Diagnostic accuracies
sens 2.5% 97.5% spec 2.5% 97.5%
[1,] 0.979 0.828 0.998 0.048 0.011 0.185
[2,] 0.938 0.769 0.985 0.242 0.125 0.415
[3,] 0.911 0.750 0.972 0.673 0.589 0.747
[4,] 0.950 0.655 0.995 0.543 0.349 0.726
[5,] 0.850 0.541 0.965 0.935 0.760 0.985
Test for equality of sensitivities:
X-squared = 2.1747, df = 4, p-value = 0.704
Test for equality of specificities:
X-squared = 65.2807, df = 4, p-value = 2.25e-13
Diagnostic OR and likelihood ratios
DOR 2.5% 97.5% posLR 2.5% 97.5% negLR 2.5% 97.5%
[1,] 2.390 0.093 61.399 1.029 0.932 1.135 0.431 0.018 10.107
[2,] 4.787 0.756 30.322 1.237 0.988 1.547 0.258 0.049 1.372
[3,] 20.986 5.449 80.832 2.784 2.126 3.647 0.133 0.040 0.436
[4,] 22.619 1.172 436.417 2.081 1.303 3.323 0.092 0.006 1.407
[5,] 81.222 7.380 893.939 13.033 2.714 62.584 0.160 0.037 0.704
Correlation of sensitivities and false positive rates:
rho 2.5 % 97.5 %
0.901 0.091 0.994
mada::forest(md1, type = "sens", main = "Sensibilidad", xlab = "Sensibilidad")
mada::forest(md1, type = "spec", main = "Especificidad")
mada::forest(md1, type = "DOR", log = TRUE, main = "Log DOR")
DesviaciĆ³nDelCanalMandibular 2 10
md2 <- df %>%
select(autor,
signo,
gold_standard,
test,
TP = vp,
FP = fp,
TN = vn,
FN = fn) %>%
filter(TP > 0) %>%
filter(test == "OPG") %>%
filter(signo == "DesviaciĆ³nDelCanalMandibular") %>%
madad(., level = 0.95)Unknown or uninitialised column: 'names'.There are very few primary studies!md2 Descriptive summary of . with 4 primary studies.
Confidence level for all calculations set to 95 %
Using a continuity correction of 0.5 if applicable
Diagnostic accuracies
sens 2.5% 97.5% spec 2.5% 97.5%
[1,] 0.979 0.828 0.998 0.048 0.011 0.185
[2,] 0.696 0.511 0.834 0.732 0.638 0.810
[3,] 0.964 0.859 0.992 0.025 0.003 0.200
[4,] 0.950 0.655 0.995 0.543 0.349 0.726
Test for equality of sensitivities:
X-squared = 15.9214, df = 3, p-value = 0.00118
Test for equality of specificities:
X-squared = 64.8844, df = 3, p-value = 5.31e-14
Diagnostic OR and likelihood ratios
DOR 2.5% 97.5% posLR 2.5% 97.5% negLR 2.5% 97.5%
[1,] 2.390 0.093 61.399 1.029 0.932 1.135 0.431 0.018 10.107
[2,] 6.276 2.501 15.753 2.602 1.731 3.910 0.415 0.234 0.736
[3,] 0.692 0.027 17.781 0.989 0.903 1.083 1.429 0.061 33.538
[4,] 22.619 1.172 436.417 2.081 1.303 3.323 0.092 0.006 1.407
Correlation of sensitivities and false positive rates:
rho 2.5 % 97.5 %
0.787 -0.714 0.995 mada::forest(md1, type = "sens", main = "Sensibilidad", xlab = "Sensibilidad")
mada::forest(md2, type = "spec", main = "Especificidad")
mada::forest(md2, type = "DOR", log = TRUE, main = "Log DOR")
DilaceraciĆ³nDeRaices 1 0
Sin artĆculos para OPG
EstrechamientoDeLasRaices 0 4
md4
Descriptive summary of . with 2 primary studies.
Confidence level for all calculations set to 95 %
Using a continuity correction of 0.5 if applicable
Diagnostic accuracies
sens 2.5% 97.5% spec 2.5% 97.5%
[1,] 0.696 0.511 0.834 0.732 0.638 0.810
[2,] 0.950 0.655 0.995 0.543 0.349 0.726
Test for equality of sensitivities:
X-squared = 1.406, df = 1, p-value = 0.236
Test for equality of specificities:
X-squared = 2.3197, df = 1, p-value = 0.128
Diagnostic OR and likelihood ratios
DOR 2.5% 97.5% posLR 2.5% 97.5% negLR 2.5% 97.5%
[1,] 6.276 2.501 15.753 2.602 1.731 3.910 0.415 0.234 0.736
[2,] 22.619 1.172 436.417 2.081 1.303 3.323 0.092 0.006 1.407
Correlation of sensitivities and false positive rates: Error in CIrho(x$cor_sens_fpr, x$nobs) : rho < 1 is not TRUE
EstrechamientoDelCanalMandibular 1 4
md5
Descriptive summary of . with 2 primary studies.
Confidence level for all calculations set to 95 %
Using a continuity correction of 0.5 if applicable
Diagnostic accuracies
sens 2.5% 97.5% spec 2.5% 97.5%
[1,] 0.979 0.828 0.998 0.048 0.011 0.185
[2,] 0.964 0.859 0.992 0.025 0.003 0.200
Test for equality of sensitivities:
X-squared = 0, df = 1, p-value = 1
Test for equality of specificities:
X-squared = 0, df = 1, p-value = 1
Diagnostic OR and likelihood ratios
DOR 2.5% 97.5% posLR 2.5% 97.5% negLR 2.5% 97.5%
[1,] 2.390 0.093 61.399 1.029 0.932 1.135 0.431 0.018 10.107
[2,] 0.692 0.027 17.781 0.989 0.903 1.083 1.429 0.061 33.538
Correlation of sensitivities and false positive rates: Error in CIrho(x$cor_sens_fpr, x$nobs) : rho > -1 is not TRUE
InterrupciĆ³nDeCorticalCanalMandibular 2 10
md6
Descriptive summary of . with 4 primary studies.
Confidence level for all calculations set to 95 %
Using a continuity correction of 0.5 if applicable
Diagnostic accuracies
sens 2.5% 97.5% spec 2.5% 97.5%
[1,] 0.979 0.828 0.998 0.048 0.011 0.185
[2,] 0.696 0.511 0.834 0.732 0.638 0.810
[3,] 0.964 0.859 0.992 0.025 0.003 0.200
[4,] 0.950 0.655 0.995 0.543 0.349 0.726
Test for equality of sensitivities:
X-squared = 15.9214, df = 3, p-value = 0.00118
Test for equality of specificities:
X-squared = 64.8844, df = 3, p-value = 5.31e-14
Diagnostic OR and likelihood ratios
DOR 2.5% 97.5% posLR 2.5% 97.5% negLR 2.5% 97.5%
[1,] 2.390 0.093 61.399 1.029 0.932 1.135 0.431 0.018 10.107
[2,] 6.276 2.501 15.753 2.602 1.731 3.910 0.415 0.234 0.736
[3,] 0.692 0.027 17.781 0.989 0.903 1.083 1.429 0.061 33.538
[4,] 22.619 1.172 436.417 2.081 1.303 3.323 0.092 0.006 1.407
Correlation of sensitivities and false positive rates:
rho 2.5 % 97.5 %
0.787 -0.714 0.995 
OscurecimientoDeLasRaices 1 9
md7
Descriptive summary of . with 4 primary studies.
Confidence level for all calculations set to 95 %
Using a continuity correction of 0.5 if applicable
Diagnostic accuracies
sens 2.5% 97.5% spec 2.5% 97.5%
[1,] 0.979 0.828 0.998 0.048 0.011 0.185
[2,] 0.696 0.511 0.834 0.732 0.638 0.810
[3,] 0.964 0.859 0.992 0.025 0.003 0.200
[4,] 0.950 0.655 0.995 0.543 0.349 0.726
Test for equality of sensitivities:
X-squared = 15.9214, df = 3, p-value = 0.00118
Test for equality of specificities:
X-squared = 64.8844, df = 3, p-value = 5.31e-14
Diagnostic OR and likelihood ratios
DOR 2.5% 97.5% posLR 2.5% 97.5% negLR 2.5% 97.5%
[1,] 2.390 0.093 61.399 1.029 0.932 1.135 0.431 0.018 10.107
[2,] 6.276 2.501 15.753 2.602 1.731 3.910 0.415 0.234 0.736
[3,] 0.692 0.027 17.781 0.989 0.903 1.083 1.429 0.061 33.538
[4,] 22.619 1.172 436.417 2.081 1.303 3.323 0.092 0.006 1.407
Correlation of sensitivities and false positive rates:
rho 2.5 % 97.5 %
0.787 -0.714 0.995
OscurecimientoYDobleContornoDeRaices 1 4
md8
Descriptive summary of . with 2 primary studies.
Confidence level for all calculations set to 95 %
Using a continuity correction of 0.5 if applicable
Diagnostic accuracies
sens 2.5% 97.5% spec 2.5% 97.5%
[1,] 0.964 0.859 0.992 0.025 0.003 0.200
[2,] 0.950 0.655 0.995 0.543 0.349 0.726
Test for equality of sensitivities:
X-squared = 0, df = 1, p-value = 1
Test for equality of specificities:
X-squared = 11.2867, df = 1, p-value = 0.000781
Diagnostic OR and likelihood ratios
DOR 2.5% 97.5% posLR 2.5% 97.5% negLR 2.5% 97.5%
[1,] 0.692 0.027 17.781 0.989 0.903 1.083 1.429 0.061 33.538
[2,] 22.619 1.172 436.417 2.081 1.303 3.323 0.092 0.006 1.407
Correlation of sensitivities and false positive rates: Error in CIrho(x$cor_sens_fpr, x$nobs) : rho < 1 is not TRUE
SuperposiciĆ³nRaicesEnCanalMandibular 1 2
md9
Descriptive summary of . with 1 primary studies.
Confidence level for all calculations set to 95 %
Using a continuity correction of 0.5 if applicable
Diagnostic accuracies
sens 2.5% 97.5% spec 2.5% 97.5%
[1,] 0.964 0.859 0.992 0.025 0.003 0.2
Test for equality of sensitivities:
X-squared = 34.381, df = 1, p-value = 4.53e-09
Test for equality of specificities:
X-squared = 16.2, df = 1, p-value = 5.7e-05
Diagnostic OR and likelihood ratios
DOR 2.5% 97.5% posLR 2.5% 97.5% negLR 2.5% 97.5%
[1,] 0.692 0.027 17.781 0.989 0.903 1.083 1.429 0.061 33.538
Correlation of sensitivities and false positive rates: Error in CIrho(x$cor_sens_fpr, x$nobs) : rho < 1 is not TRUE
---
title: "2019 Systematic review Panoramic Alveolar Nerve Damage"
output: 
  html_notebook: 
    toc: yes
    toc_float: true
    fig_caption: true
---
# Packages
```{r}
library(tidyverse)
# install.packages("mada")
# install.packages("meta")
library(mada)
library(meta)
```

# Dataset
```{r, include=FALSE}
df <- read_csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vT56eblHzQ7b0rj5FgxtyToGeLIiBohMuTJn_8MeVDnVy0z6L3CsmNYEZDa1jWBVQ/pub?gid=558873082&single=true&output=csv") %>% 
  select(AUTOR:TEST)
df <- df[-58, ]
```
```{r}
# install.packages("rapportools")
df$SIGNO <- rapportools::tocamel(df$SIGNO) 
df <- df %>% 
  mutate(SIGNO = case_when(
    SIGNO == "InterrupciónDeLaCorticalCanalMandibular" ~ "InterrupciónDeCorticalCanalMandibular",
    SIGNO == "OscurecimientoDeLaRaiz" ~ "OscurecimientoDeLasRaices", 
    TRUE ~ as.character(SIGNO)
  ))
```

```{r}
df <- janitor::clean_names(df)
```


```{r}
aNumero <- function(x, na.rm = FALSE) (as.integer(x))
df <- df %>% 
  mutate_at(c("a_seleccion_pacientes", 
              "a_referencia_estandar", 
              "especificidad"), aNumero)
```


# EDA

```{r}
table(df$autor)
```

## Calidad

```{r}
df %>% 
  mutate(riesgoDeSesgo = rs_seleccion_paciente + rs_test_indice + rs_referencia_estandar) %>% 
  mutate(aplicabilidad = a_flujo_y_tiempo + a_seleccion_pacientes + a_test_indice + a_referencia_estandar) %>% 
  group_by(autor) %>% 
  summarise(sesgo = mean(riesgoDeSesgo), aplicab = mean(aplicabilidad))
```


# Meta-analysis

```{r}
table(df$signo)
```

```{r}
df %>% 
  mutate_if(is.character, funs(factor(trimws(.)))) %>% 
  group_by(signo) %>% 
  summarise(n = n())
```

Creo los totales
```{r}
df <- df %>% 
  mutate(n1 = vp + fp, 
         n2 = vn + fn)
```

```{r}
md1 <- df %>% 
  select(autor, 
         TP = vp, 
         FP = fp, 
         TN = vn, 
         FN = fn) %>% 
  na.omit() %>% 
  madad(., level = 0.95)
```
```{r}
print(md1, digits = 2)
```
```{r}
oldpar <- par(mfrow=c(1, 2), pty="m")
```




## Desviación raíces / clinica
```{r}
md1 <- df %>% 
  filter(signo == "DesviaciónDeLasRaices") %>% 
  select(autor, 
         TP = vp, 
         FP = fp, 
         TN = vn, 
         FN = fn) %>% 
  na.omit() %>% 
  madad(., level = 0.95)
md1  


```
```{r}

mada::forest(md1, type = "sens", main = "Sensibilidad", xlab = "Sensibilidad")
```
```{r}
mada::forest(md1, type = "spec", main = "Especificidad")
```
```{r}
mada::forest(md1, type = "DOR", log = TRUE,  main = "Log DOR")
```
  
## DesviaciónDelCanalMandibular             2                 10

```{r}
md2 <- df %>%
  select(autor,
         signo, 
         gold_standard, 
         test, 
         TP = vp, 
         FP = fp, 
         TN = vn, 
         FN = fn) %>%
  filter(TP > 0) %>% 
  filter(test == "OPG") %>% 
  filter(signo == "DesviaciónDelCanalMandibular") %>%
  madad(., level = 0.95)
md2  


```
```{r}
mada::forest(md1, type = "sens", main = "Sensibilidad", xlab = "Sensibilidad")
```
```{r}
mada::forest(md2, type = "spec", main = "Especificidad")
```
```{r}
mada::forest(md2, type = "DOR", log = TRUE,  main = "Log DOR")
```
 


## DilaceraciónDeRaices                     1                  0
 Sin artículos para OPG

##  EstrechamientoDeLasRaices                0                  4
```{r}
md4 <- df %>%
  select(autor,
         signo, 
         gold_standard, 
         test, 
         TP = vp, 
         FP = fp, 
         TN = vn, 
         FN = fn) %>%
  filter(TP > 0) %>% 
  filter(test == "OPG") %>% 
  filter(signo == "EstrechamientoDeLasRaices") %>%
  madad(., level = 0.95)
md4  


```
```{r}
mada::forest(md4, type = "sens", main = "Sensibilidad", xlab = "Sensibilidad")
```
```{r}
mada::forest(md4, type = "spec", main = "Especificidad")
```
```{r}
mada::forest(md4, type = "DOR", log = TRUE,  main = "Log DOR")
```
  
##  EstrechamientoDelCanalMandibular         1                  4

```{r}
md5 <- df %>%
  select(autor,
         signo, 
         gold_standard, 
         test, 
         TP = vp, 
         FP = fp, 
         TN = vn, 
         FN = fn) %>%
  filter(TP > 0) %>% 
  filter(test == "OPG") %>% 
  filter(signo == "EstrechamientoDelCanalMandibular") %>%
  madad(., level = 0.95)
md5  


```
```{r}
mada::forest(md5, type = "sens", main = "Sensibilidad", xlab = "Sensibilidad")
```
```{r}
mada::forest(md5, type = "spec", main = "Especificidad")
```
```{r}
mada::forest(md5, type = "DOR", log = TRUE,  main = "Log DOR")
```


##  InterrupciónDeCorticalCanalMandibular    2                 10

```{r}
md6 <- df %>%
  select(autor,
         signo, 
         gold_standard, 
         test, 
         TP = vp, 
         FP = fp, 
         TN = vn, 
         FN = fn) %>%
  filter(TP > 0) %>% 
  filter(test == "OPG") %>% 
  filter(signo == "InterrupciónDeCorticalCanalMandibular") %>%
  madad(., level = 0.95)
md6  


```
```{r}
mada::forest(md6, type = "sens", main = "Sensibilidad", xlab = "Sensibilidad")
```
```{r}
mada::forest(md6, type = "spec", main = "Especificidad")
```
```{r}
mada::forest(md6, type = "DOR", log = TRUE,  main = "Log DOR")
```


##  OscurecimientoDeLasRaices                1                  9

```{r}
md7 <- df %>%
  select(autor,
         signo, 
         gold_standard, 
         test, 
         TP = vp, 
         FP = fp, 
         TN = vn, 
         FN = fn) %>%
  filter(TP > 0) %>% 
  filter(test == "OPG") %>% 
  filter(signo == "OscurecimientoDeLasRaices") %>%
  madad(., level = 0.95)
md7  


```
```{r}
mada::forest(md7, type = "sens", main = "Sensibilidad", xlab = "Sensibilidad")
```
```{r}
mada::forest(md7, type = "spec", main = "Especificidad")
```
```{r}
mada::forest(md7, type = "DOR", log = TRUE,  main = "Log DOR")
```

##  OscurecimientoYDobleContornoDeRaices     1                  4

```{r}
md8 <- df %>%
  select(autor,
         signo, 
         gold_standard, 
         test, 
         TP = vp, 
         FP = fp, 
         TN = vn, 
         FN = fn) %>%
  filter(TP > 0) %>% 
  filter(test == "OPG") %>% 
  filter(signo == "OscurecimientoYDobleContornoDeRaices") %>%
  madad(., level = 0.95)
md8  


```
```{r}
mada::forest(md8, type = "sens", main = "Sensibilidad", xlab = "Sensibilidad")
```
```{r}
mada::forest(md8, type = "spec", main = "Especificidad")
```
```{r}
mada::forest(md8, type = "DOR", log = TRUE,  main = "Log DOR")
```
 

##  SuperposiciónRaicesEnCanalMandibular     1                  2

```{r}
md9 <- df %>%
  select(autor,
         signo, 
         gold_standard, 
         test, 
         TP = vp, 
         FP = fp, 
         TN = vn, 
         FN = fn) %>%
  filter(TP > 0) %>% 
  filter(test == "OPG") %>% 
  filter(signo == "SuperposiciónRaicesEnCanalMandibular") %>%
  madad(., level = 0.95)
md9  


```
```{r}
mada::forest(md9, type = "sens", main = "Sensibilidad", xlab = "Sensibilidad")
```
```{r}
mada::forest(md9, type = "spec", main = "Especificidad")
```
```{r}
mada::forest(md9, type = "DOR", log = TRUE,  main = "Log DOR")
```
