Primary data collection in health technology assessment
Bibliographic record
Abstract
This study discusses the value of primary data collection as part of health technology assessment (HTA). Primary data collection can help reduce uncertainty in HTA and better inform evidence-based decision making. However, methodological issues such as choosing appropriate study design and practical concerns such as the value of collecting additional information need to be addressed. The authors emphasize the conditions required for successful primary data collection in HTA: experienced researchers, sufficient funding, and coordination among stakeholders, government, and researchers. The authors conclude that, under specific conditions, primary data collection is a worthwhile endeavor in the HTA process.
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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 | 0.445 | 0.631 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.014 | 0.025 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".