Packages

# library(tidyverse)
# install.packages("mada")
# install.packages("meta")
pacman::p_load(mada, meta, tidyverse, rapportools)
# library(mada)
# library(meta)

Dataset

Filtro solamente pra OPG y que tengan para calcular sens y espec

df <- df %>% 
  filter(test == "OPG", vp != "NA")

glimpse(df)

Observations: 21
Variables: 25
$ autor                  [3m[38;5;246m<chr>[39m[23m "H. GHAEMINIA", "H. GHAEMINIA", "H. GHAEMINIA", "H. GHAEMINIA", "H. GHAEMINIA", "H.…
$ rs_seleccion_paciente  [3m[38;5;246m<dbl>[39m[23m -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1
$ rs_test_indice         [3m[38;5;246m<dbl>[39m[23m -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1
$ rs_referencia_estandar [3m[38;5;246m<dbl>[39m[23m -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1
$ a_flujo_y_tiempo       [3m[38;5;246m<dbl>[39m[23m -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
$ a_seleccion_pacientes  [3m[38;5;246m<dbl>[39m[23m -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1
$ a_test_indice          [3m[38;5;246m<dbl>[39m[23m -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1
$ a_referencia_estandar  [3m[38;5;246m<dbl>[39m[23m -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1
$ muestra_opg            [3m[38;5;246m<dbl>[39m[23m 53, 53, 53, 53, 53, 53, 161, 161, 161, 161, 60, 60, 60, 60, 60, 31, 31, 31, 31, 31,…
$ signo_identificado     [3m[38;5;246m<chr>[39m[23m "Interrupción linea blanca", "Oscurecimiento raiz", "Desvio canal alveolar inferior…
$ signo_identificado_2   [3m[38;5;246m<dbl>[39m[23m NA, NA, NA, NA, NA, NA, 62, 30, 15, 5, 1, NA, 28, 3, 8, NA, NA, NA, NA, NA, NA
$ muestra_cbct           [3m[38;5;246m<dbl>[39m[23m 53, 53, 53, 53, 53, 53, 161, 161, 161, 161, 41, 41, 41, 41, 41, 31, 31, 31, 31, 31,…
$ contacto_cbct          [3m[38;5;246m<chr>[39m[23m "45", "45", "45", "45", "45", "45", "27", "27", "27", "27", "41", "41", "41", "41",…
$ contacto_real_iac      [3m[38;5;246m<dbl>[39m[23m 23, 23, 23, 23, 23, 23, 25, 25, 25, 25, 41, 41, 41, 41, 41, 3, 3, 3, 3, 3, 3
$ ppv                    [3m[38;5;246m<dbl>[39m[23m 0.44, 0.44, 0.44, 0.44, 0.44, 0.00, 0.31, 0.31, 0.31, 0.31, NA, NA, NA, NA, NA, NA,…
$ npv                    [3m[38;5;246m<dbl>[39m[23m 1.00, 1.00, 1.00, 1.00, 1.00, 0.00, 0.98, 0.98, 0.98, 0.98, NA, NA, NA, NA, NA, NA,…
$ vp                     [3m[38;5;246m<dbl>[39m[23m 23, 23, 23, 23, 23, 0, 19, 19, 19, 19, 40, 40, 40, 40, 40, 9, 9, 9, 9, 9, 9
$ fp                     [3m[38;5;246m<dbl>[39m[23m 29, 29, 29, 29, 29, 0, 43, 43, 43, 43, 19, 19, 19, 19, 19, 10, 10, 10, 10, 10, 10
$ vn                     [3m[38;5;246m<dbl>[39m[23m 1, 1, 1, 1, 1, 0, 72, 72, 72, 72, 0, 0, 0, 0, 0, 12, 12, 12, 12, 12, 12
$ fn                     [3m[38;5;246m<dbl>[39m[23m 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0
$ accuracy               [3m[38;5;246m<dbl>[39m[23m 0.45, 0.45, 0.45, 0.45, 0.45, 0.00, 0.64, 0.64, 0.64, 0.64, NA, NA, NA, NA, NA, NA,…
$ test                   [3m[38;5;246m<chr>[39m[23m "OPG", "OPG", "OPG", "OPG", "OPG", "OPG", "OPG", "OPG", "OPG", "OPG", "OPG", "OPG",…
$ gold_standard          [3m[38;5;246m<chr>[39m[23m "Evaluación clínica", "Evaluación clínica", "Evaluación clínica", "Evaluación clíni…
$ sensibilidad_opg       [3m[38;5;246m<dbl>[39m[23m 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 0.70, 0.70, 0.70, 0.70, 0.97, 0.97, 0.97, 0.97,…
$ especificidad          [3m[38;5;246m<dbl>[39m[23m 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.77, 0.77, 0.77, 0.77, 0.00, 0.00, 0.00, 0.00,…

df$signo_identificado <- rapportools::tocamel(df$signo_identificado) 
df <- df %>% 
  mutate(signo_identificado = case_when(
    signo_identificado == "InterrupciónDeLaCorticalCanalMandibular" ~ "InterrupciónDeCorticalCanalMandibular",
    signo_identificado == "OscurecimientoDeLaRaiz" ~ "OscurecimientoDeLasRaices", 
    TRUE ~ as.character(signo_identificado)
  ))

table(df$signo_identificado)


        DesvioCanalAlveolarInferior EstrechamientoCanalAlveolarInferior                  EstrechamientoRaiz 
                                  4                                   2                                   3 
            InterrupciónLineaBlanca                  OscurecimientoRaiz                     RaicesDesviadas 
                                  4                                   4                                   2 
                  RaizOscuraYBifida 
                                  2

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.JHAMB   H. GHAEMINIA TANTANAPORNKUL 
             5              6              6              4

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_identificado)


        DesvioCanalAlveolarInferior EstrechamientoCanalAlveolarInferior                  EstrechamientoRaiz 
                                  4                                   2                                   3 
            InterrupciónLineaBlanca                  OscurecimientoRaiz                     RaicesDesviadas 
                                  4                                   4                                   2 
                  RaizOscuraYBifida 
                                  2

Creo los totales

df <- df %>% 
  mutate(n1 = vp + fp, 
         n2 = vn + fn)

table(df$signo_identificado)


        DesvioCanalAlveolarInferior EstrechamientoCanalAlveolarInferior                  EstrechamientoRaiz 
                                  4                                   2                                   3 
            InterrupciónLineaBlanca                  OscurecimientoRaiz                     RaicesDesviadas 
                                  4                                   4                                   2 
                  RaizOscuraYBifida 
                                  2

Desviación raíces / clinica

md1 <- df %>% 
  filter(signo_identificado == "DesvioCanalAlveolarInferior") %>% 
  select(autor, 
         TP = vp, 
         FP = fp, 
         TN = vn, 
         FN = fn) %>% 
  na.omit() %>% 
  madad(., level = 0.95)

Unknown or uninitialised column: 'names'.There are very few primary studies!

md1

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 0.625 0.534 0.708
[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 = 49.7698, df = 3, p-value = 8.94e-11


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,]  3.824 1.572   9.301 1.857 1.323 2.607 0.486 0.272  0.866
[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 % 
 0.713 -0.789  0.993

mada::forest(md1, type = "sens", main = "Sensibilidad DesvioCanalAlveolarInferior", xlab = "Sensibilidad")

mada::forest(md1, type = "spec", main = "Especificidad DesvioCanalAlveolarInferior")

mada::forest(md1, type = "DOR", log = TRUE,  main = "Log DOR DesvioCanalAlveolarInferior")

EstrechamientoCanalAlveolarInferior

md2 <- df %>%
  select(autor,
         signo_identificado, 
         gold_standard, 
         test, 
         TP = vp, 
         FP = fp, 
         TN = vn, 
         FN = fn) %>%
  filter(TP > 0) %>% 
  filter(test == "OPG") %>% 
  filter(signo_identificado == "EstrechamientoCanalAlveolarInferior") 
md2  <- mada::madad(md2, level = 0.95)

Unknown or uninitialised column: 'names'.There are very few primary studies!

mada::forest(md2, type = "sens", main = "Sensibilidad", xlab = "Sensibilidad EstrechamientoCanalAlveolarInferior")

mada::forest(md2, type = "spec", main = "Especificidad EstrechamientoCanalAlveolarInferior")

mada::forest(md2, type = "DOR", log = TRUE,  main = "Log DOR EstrechamientoCanalAlveolarInferior")

EstrechamientoRaiz

md4 <- df %>%
  select(autor,
         signo_identificado, 
         gold_standard, 
         test, 
         TP = vp, 
         FP = fp, 
         TN = vn, 
         FN = fn) %>%
  filter(TP > 0) %>% 
  filter(test == "OPG") %>% 
  filter(signo_identificado == "EstrechamientoRaiz") %>%
  madad(., level = 0.95)

Unknown or uninitialised column: 'names'.There are very few primary studies!

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 0.625 0.534 0.708
[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 = 0.2486, df = 1, p-value = 0.618


Diagnostic OR and likelihood ratios 
        DOR  2.5%   97.5% posLR  2.5% 97.5% negLR  2.5% 97.5%
[1,]  3.824 1.572   9.301 1.857 1.323 2.607 0.486 0.272 0.866
[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

InterrupciónLineaBlanca

md5

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 0.625 0.534 0.708
[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 = 49.7698, df = 3, p-value = 8.94e-11


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,]  3.824 1.572   9.301 1.857 1.323 2.607 0.486 0.272  0.866
[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 % 
 0.713 -0.789  0.993

OscurecimientoRaiz

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 0.625 0.534 0.708
[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 = 49.7698, df = 3, p-value = 8.94e-11


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,]  3.824 1.572   9.301 1.857 1.323 2.607 0.486 0.272  0.866
[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 % 
 0.713 -0.789  0.993

RaicesDesviadas

md7

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,] 0.950 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 = 14.3731, df = 1, p-value = 0.00015


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,] 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

RaizOscuraYBifida

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

---
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")
pacman::p_load(mada, meta, tidyverse, rapportools)
# library(mada)
# library(meta)
```

# Dataset
```{r, include=FALSE}

df <- read_csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vQRwpwRu1qHLDHwhQRePpBXVO5jMTD3BYD6NkX1t9IxiF1A4hciEwUiGxt0hy6KC-km45cJ5W9EahY8/pub?gid=567928337&single=true&output=csv")


df <- janitor::clean_names(df)
```


Filtro solamente pra OPG y que tengan para calcular sens y espec

```{r}
df <- df %>% 
  filter(test == "OPG", vp != "NA")
```

```{r}
glimpse(df)
```


```{r}
df$signo_identificado <- rapportools::tocamel(df$signo_identificado) 
df <- df %>% 
  mutate(signo_identificado = case_when(
    signo_identificado == "InterrupciónDeLaCorticalCanalMandibular" ~ "InterrupciónDeCorticalCanalMandibular",
    signo_identificado == "OscurecimientoDeLaRaiz" ~ "OscurecimientoDeLasRaices", 
    TRUE ~ as.character(signo_identificado)
  ))
```

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


```{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_identificado)
```


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




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

## Desviación raíces / clinica
```{r}
md1 <- df %>% 
  filter(signo_identificado == "DesvioCanalAlveolarInferior") %>% 
  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 DesvioCanalAlveolarInferior", xlab = "Sensibilidad")
```
```{r}
mada::forest(md1, type = "spec", main = "Especificidad DesvioCanalAlveolarInferior")
```
```{r}
mada::forest(md1, type = "DOR", log = TRUE,  main = "Log DOR DesvioCanalAlveolarInferior")
```
  
## EstrechamientoCanalAlveolarInferior

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


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


## EstrechamientoRaiz  


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

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

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


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


##  OscurecimientoRaiz

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


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


##  RaicesDesviadas

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


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

##  RaizOscuraYBifida

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


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


2019 Systematic review Panoramic Alveolar Nerve Damage