Potencial sesgo de patrocinio en los análisis coste-efectividad de intervenciones sanitarias: un análisis transversal
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
OBJECTIVE: To examine the relationship between the funding source of cost-effectiveness analyses of healthcare interventions published in Spain and study conclusions. DESIGN: Descriptive cross-sectional study. LOCATION: Scientific literature databases (until December 2014). PARTICIPANTS (ANALYSIS UNITS): Cohort of cost-effectiveness analysis of healthcare interventions published in Spain between 1989-2014 (n=223) presenting quality-adjusted life years (QALYs) as the outcome measure. MAIN MEASUREMENTS: The relationship between qualitative conclusions of the studies and the type of funding source were established using Fisher's exact test in contingency tables. Distributions of the incremental cost-effectiveness ratios by source of funding in relation to hypothetical willingness to pay thresholds between €30,000-€50,000 per QALY were explored. RESULTS: A total of 136 (61.0%) studies were funded by industry. The industry-funded studies were less likely to report unfavorable or neutral conclusions than studies non-funded by industry (2.2% vs. 23.0%; P<.0001), largely driven by studies evaluating drugs (0.9% vs. 21.4%; P<.0001). The incremental cost-effectiveness ratios in studies funded by industry were more likely to be below the hypothetical willingness to pay threshold of €30,000 (73.8% vs. 56.3%; P<.0001) and €50,000 (89.4% vs. 68.2%; P<.0001) per QALY. CONCLUSIONS: This study reveals a potential sponsorship bias in cost-effectiveness analyses of healthcare interventions. Studies funded by industry could be favoring the efficiency profile of their products.
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 | 0.020 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it