Apreciación de artículos científicos sobre la evaluación clínica del Trastorno del Espectro Autista en el continente americano
Bibliographic record
Abstract
El objetivo de este estudio es proporcionar información detallada sobre los diagnósticos clínicos de Trastorno de Espectro Autista que se han realizado recientemente para detectar de manera puntual avances, así como también áreas de oportunidad. Para ello se empleó una investigación documental en la que se seleccionaron y analizaron 16 artículos científicos publicados en el continente americano que trataban exclusivamente del diagnóstico clínico del TEA basado en la última edición del Manual de diagnóstico estadístico de los trastornos mentales (DSM-5). Los resultados cualitativos muestran que la mayoría de las publicaciones científicas relacionadas con el diagnóstico clínico del TEA se realizan en los Estados Unidos, siendo el diagnóstico en edad tempranas el tema más estudiado.
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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 | 0.087 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.023 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".