Differences between systematic reviews and health technology assessments: A trade-off between the ideals of scientific rigor and the realities of policy making
Bibliographic record
Abstract
OBJECTIVES: To elucidate important differences between a health technology assessment (HTA) and a systematic review, using an HTA of positron emission tomography (PET) as an example. METHODS: Interviews with seventeen individuals who were authors or users of the PET HTA. RESULTS: Those interviewed identified seven areas in which HTAs often differ from traditional systematic reviews: (i) methodological standards (HTAs may include literature of relatively poor methodological quality if a topic is of importance to decision-makers), (ii) replication of previous studies (relatively common for HTAs but not systematic reviews), (iii) choice of topics (more policy oriented for HTAs, while systematic reviews tend to be driven by researcher interest), (iv) inclusion of content experts and policy-makers as authors (policy-makers more likely to be included in HTAs, although there are potential conflicts of interest), (v) inclusion of economic evaluations (more often with HTAs, although economic evaluations based upon poor clinical data may not be useful), (vi) making policy recommendations (more likely with HTAs, although this must be done with caution), and (vii) dissemination of the report (more often actively done for HTAs). CONCLUSIONS: This case study of an HTA of PET scanning confirms that HTAs are a bridge between science and policy and require a balance between the ideals of scientific rigor and the realities of policy making.
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 | 0.876 | 0.940 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.034 | 0.028 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.039 | 0.048 |
| Open science | 0.010 | 0.027 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".