Alexitimia, familia y trastornos alimentarios
Bibliographic record
Abstract
Objetivo: Contrastar las diferencias en el TAS-20 \nentre familias con una hija con un trastorno alimentario \n(TA) y un grupo control. Método: una muestra de 64 mujeres \nque presentaban un TA según criterios diagnósticos del \nDSM-IV (APA, 1994) [26 con anorexia nerviosa restrictiva \n(ANR), 14 con anorexia nerviosa bulímica (ANB) y 29 con \nbulimia nerviosa (BN)] complementada con un grupo \n“control” formado por 55 mujeres, sus padres y madres, \ncon similares características sociodemográficas fue evaluada \ncon la Toronto Alexithymia Scale (TAS-20). Resultados: \nlas hijas con TA, así como sus padres y madres, presentan \npuntuaciones mayores en el TAS-20 que el grupo control. \nLa alexitimia de las hijas está asociada a las de sus padres y \nmadres. Discusión: la alexitimia parece ser un característica \nde las familias con TA, quedando por dilucidar si es un rasgo \ny juega un papel en el desarrollo del trastorno o es un \nestado debido al mismo.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".