Estándares Consolidados de Reporte de Evaluaciones Económicas Sanitarias: adaptación al español de la lista de comprobación CHEERS 2022
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: 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. METHODS: 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. RESULTS: In this article, we detail the process and the Spanish adaptation of the 28-item CHEERS 2022 checklist and its recommendations. CONCLUSIONS: This list is intended for researchers reporting HEE in peer-reviewed journals and reviewers, editors, and, among others, health technology assessment bodies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.071 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it