Redefining Health Technology Assessment: A Comment on “The New Definition of Health Technology Assessment: A Milestone in International Collaboration”
Bibliographic record
Abstract
Abstract A new definition of health technology assessment (HTA), developed by an International Joint Task Group claims to be a “milestone,” “an historic achievement,” and “a cornerstone reference”—claims that we think to be unjustified. We too favor clear definitions, especially when confusion abounds. However, the Task Group seems to have developed a definition without the help of usual conventions regarding definitions and, in our view, through an ill-described process. A definition ought to differentiate the entity defined from other entities. This one fails to do so. It states traits that are true of HTA (e.g., that is interdisciplinary) but HTA is not alone in this. There are other concerns: examples of HTA’s use are embodied in the definition, precluding other uses; the adjectives used, although generally true of HTA, are not differentiating features; and attributing to HTA specific purposes, thereby excluding other purposes. We have sympathy for these purposes but cannot consider them HTA’sonlypurposes or even, itsmainpurpose. A newcomer to HTA, on reading this definition, will have no idea of HTA’s true potential. These numerous failings, we feel, send all the wrong signals, and could ultimately weaken, rather than strengthen perceptions of HTA’s legitimacy and objectivity. The production of a good definition remains, therefore, a work in progress.
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 | 0.089 | 0.180 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.018 | 0.043 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.013 | 0.014 |
| Research integrity | 0.104 | 0.151 |
| 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".