Niveles de alexitimia según severidad de sintomatología depresiva en pacientes con depresión
Bibliographic record
Abstract
La presente investigacion tuvo como objetivo comparar los niveles de alexitimia segun la severidad de la sintomatologia depresiva en un grupo de pacientes con depresion. Ademas, se busco comparar los diferentes grados de sintomatologia depresiva entre si, comparar los tres factores de la alexitimia segun la intensidad de la sintomatologia depresiva y comparar los niveles de alexitimia segun los datos sociodemograficos. Para dicho fin, se aplico a 51 pacientes el Inventario de Depresion de Beck (BDI-II) y Escala de Alexitimia de Toronto (TAS-20). Se encontro una alta correlacion entre depresion y alexitimia, donde los pacientes con mayor severidad de sintomatologia depresiva presentaron niveles mas altos de alexitimia. Los factores 1 y 2 del TAS-20 presentaron puntuaciones mayores, mas no se observo una relacion con el factor 3. Los resultados sugieren que los pacientes con mayor severidad de sintomatologia depresiva, tienden a puntuar mas alto en la escala de alexitimia debido a las dificultades en la identificacion y verbalizacion de las emociones que se encuentran en ambas, ademas de una tendencia a somatizar todo aquello que no entienden ni pueden expresar.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".