An operationalization framework for lifecycle health technology assessment: a Health Technology Assessment International Global Policy Forum Task Force report
Bibliographic record
Abstract
Operationalization guidance is needed to support health technology assessment (HTA) bodies considering implementing lifecycle HTA (LC-HTA) approaches. The 2022 Health Technology Assessment International (HTAi) Global Policy Forum (GPF) established a Task Force to develop a position paper on LC-HTA. In its first paper, the Task Force established a definition and framework for LC-HTA in order to tailor it to specific decision problems. This second paper focused on the provision of practical operational guidance to implement LC-HTA. Detailed descriptions of the three LC-HTA operational steps are provided (defining the decision problem, sequencing of HTA activities, and developing optimization criteria) and accompanied by worked examples and an operationalization checklist with 20 different questions for HTA bodies to consider when developing an LC-HTA approach. The questions were designed to be applicable across different types of HTA and scenarios, and require adaptation to local jurisdictions, remits, and context.
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 | 0.076 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".