Bibliographic record
Abstract
In this issue of the Journal, Panteli et al. (6) provide insight into the extent to which a gender-sensitive approach is adopted by sixty health technology assessment (HTA) agencies worldwide. Their findings should make all of us involved in the production of HTA take pause: less than a handful of the agency Web sites that were examined by Panteli's team made any mention of gender as an explicit consideration in priority setting processes or in the HTA methods used (6). This is despite the fact that gender is recognized as a social determinant of health (1) and despite best practices that acknowledge the need to account for equity issues—of which gender is one—in the design, conduct, and reporting of HTA (3;4). Assuming we take the findings of Panteli et al. at face value, this does seem to be a case of “do as I say, not as I do.”
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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.024 | 0.034 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".