Health Policy 1998;19:267-88. [The author responds:]
Bibliographic record
Abstract
occur without bench research (espe-cially Canadian). But doesn’t his asser-tion overlook about 35 000 surgical, ed-ucational and health care trials, most of which required no prior bench re-search? And doesn’t he defeat his own argument with the 3 recent drug trials he does cite (2 led from Canada and the other with major Canadian collabora-tion)? PRISM-PLUS was conducted in 12 countries, HOPE in 16 and GUSTO in 10, but only 15 % of the references in these published studies were about bench research (which the investigators were comfortable to ex-trapolate from laboratories in just 8 countries), and 85 % of their cited justi-fication came from previous trials or clinical surveys. Second, the need to elucidate the molecular basis for diseases affecting our First Nations families is met by shipping appropriate specimens to the best laboratories in the world. It doesn’t require that the bench research be car-ried out in Canada; the performance of reverse transcriptase is the same in Ot-tawa, Omaha, Oslo and Oxford. In sharp contrast, the performance of the health care organizational elements that profoundly affect the compliance, co-morbidity, co-intervention, costs of care and consequent outcomes of pa-tients in randomized trials differs widely in these 4 sites, and these differences may require separate trials (and their associated economic analyses) in each country. Responding to my editorial1 on be-
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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.049 | 0.030 |
| Insufficient payload (model declined to judge) | 0.038 | 0.018 |
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".