Estándares Consolidados de Reporte de Evaluaciones Económicas Sanitarias: adaptación al español de la lista de comprobación CHEERS 2022
Bibliographic record
Abstract
Health economic evaluations (HEEs) are comparative analyses of courses of action in terms of both costs and consequences. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) original version and its adaptation to Spanish were published in 2013. Its objectives were to promote that the HEEs are identifiable, interpretable, and useful for decision making and serve as a reporting guide. The new CHEERS 2022 replaces the previous one and tries to be more easily applied to any HEE and incorporates recent methodological advances and the importance of stakeholder involvement including patients and the general public.For the present adaptation, the following stages were followed: (1) independent translations of the original list into Spanish, (2) blind back-translations, (3) evaluation of their quality, (4) preparation of a new version in Spanish, (5) review and improvement by the author team, (6) preparation of a new version in Spanish, (7) distribution of the preliminary Spanish version and the original one to the American HTA Network (Red de las Américas de Evaluación de Tecnologías Sanitarias) and Spanish-speaking experts for evaluation and feedback, (8) monitoring of changes to the original list under peer review at BritishMedicalJournal, and (9) consolidation of the final adaptation of the Spanish CHEERS 2022 checklist.In this article, we detail the process and the Spanish adaptation of the 28-item CHEERS 2022 checklist and its recommendations.This list is intended for researchers reporting HEE in peer-reviewed journals and reviewers, editors, and, among others, health technology assessment bodies.
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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 | 0.242 | 0.454 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.028 | 0.023 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".