Lifecycle HTA: promising applications and a framework for implementation. An HTAi Global Policy Forum Task Force report
Bibliographic record
Abstract
The 2022 Health Technology Assessment International (HTAi) Global Policy Forum (GPF) established the goal of developing a position statement and framework for lifecycle HTA (LC-HTA), through a Task Force leveraging multi-stakeholder monthly discussions and GPF member input. The Task Force developed a working definition: LC-HTA is a systematic process utilizing sequential HTA activities to inform decision making where the evidence base, the health technology itself, or the context in which it is applied, has a potential to meaningfully change at different points in its LC. Four key scenarios were identified where it was considered that an LC-HTA approach would add sufficient value to HTA bodies and their key stakeholders to justify the additional resource burden. Based on the four scenarios, a high-level LC-HTA framework was developed consisting of (i) defining the decision problem, (ii) sequencing of HTA activities, and (iii) developing optimization criteria. Subsequently, the Task Force developed operationalization guidance for LC-HTA in a companion paper.
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 | 0.083 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".