PD165 To What Extent Do Health Technology Assessment Bodies Cross-Reference Each Other In Their Reports?
Bibliographic record
Abstract
Introduction Due to different timing of drug launches across countries, published health technology assessment (HTA) findings from one country may impact HTA outcomes in other countries. The aim of our work was to identify the most influential HTA bodies by analyzing to what extent HTA bodies cross-reference each other in their HTA reports. Methods We analyzed the HTA reports on single drug assessments (SDA) published by 46 HTA bodies from 28 countries (and cross-country collaborations) with decision dates between January 2011 and November 2023. We searched the identified HTA reports by using natural language processing and a predefined set of keywords to identify whether, and to what extent, HTA bodies reference each other in their HTA reports. Additionally, we assessed if there is a trend over time in the cross-referencing, and whether any clusters could be identified. Results Based on the analysis of 24,793 SDAs, the National Institute for Health and Care Excellence (NICE) was referenced the most (in 4,198 HTA reports across 39 HTA bodies), followed by the Canadian Agency for Drugs and Technologies in Health (in 2,034 reports across 35 HTA bodies), and the Scottish Medicines Consortium (SMC) (in 1,960 reports across 31 HTA bodies). The HTA bodies that most often referenced other HTAs were the Agency for Health Technology Assessment and Tariff System, the Haute Autorité de santé, and NICE. Seven HTA bodies were not referenced in any HTA report, while four did not reference any other HTA body. Conclusions Our research shows that most of the analyzed HTA agencies not only referenced other HTA bodies in their HTA reports but were also referenced by other HTA bodies. The most often referenced HTA agencies were mostly from English-speaking countries, were well recognized, and had well defined methodologies.
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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 | 0.117 | 0.379 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.041 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".