Bibliographic record
Abstract
La evaluación de la tecnología en salud requiere de un fundamento económico, pero la evidencia deja importantes aspectos sujetos al criterio del investigador. Precisamente, para disminuir el sesgo personal del investigador, Australia y la provincia canadiense de Ontario, que exigen evaluaciones económicas previas al registro o reembolso de los nuevos medicamentos, han desarrollado un manual o guía para estandarizar evaluación económica (2,4). De este modo, con una metodología normalizada se garantizaría la no subjetividad en las decisiones, y se facilita la comparabilidad geográfica y temporal de los estudios. Esta iniciativa se ha seguido en otros países, como el Reino Unido, Holanda y España. La propia Comisión Europea, a través del programa BIOMED, ha impulsado un proyecto cuya finalidad es armonizar los estudios de evaluación económica. La evaluación económica se encuentra en la actualidad en una situación parecida a la de los ensayos clínicos hace más de tres décadas, cuando se sentaron las bases metodológicas para garantizar su credibilidad y para impulsar su desarrollo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.110 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".