A quantitative ranking of Canada's research output of original human studies for the decade 1989 to 1998.
Bibliographic record
Abstract
BACKGROUND: Since 1987 research articles have been catalogued with the author's affiliation address in the 40 databases of the Medical Literature Analysis and Retrieval System (MEDLARS) of the National Library of Medicine, Bethesda, Md. The present study was conducted to examine the Canadian entries in MEDLARS to interpret past and future trends and to combine the MEDLARS demographic data with data from other sources to rank Canadian research output of human studies both nationally and internationally. METHODS: The PubMed Web site of the National Library of Medicine was used to count medical articles archived in MEDLARS and published from Jan. 1, 1989, through Dec. 31, 1998. The articles attributed to Canadian authors were compared by country, province, city, medical school, hospital, article type, journal and medical specialty. RESULTS: During the study period Canadian authors contributed on average 3% (standard deviation [SD] 0.2%) of the worldwide MEDLARS content each year, which translated to a mean of 11,067 (SD 1037) articles per year; 49% were human studies, of which 13% were clinical or controlled trials, and 55% involved people aged 18 years or less. In total, 68% of the articles were by authors affiliated with Canadian medical schools; those affiliated with the University of Toronto accounted for the greatest number (8604), whereas authors affiliated with McGill University had the greatest rate of annual increase in the quantity published (8%). Over one-third (38%) of the articles appeared in Canadian journals. When counted by specialty, 17% of the articles were by authors with clinical specialties, 5% by those with surgical specialties and 3% by those with laboratory specialties. INTERPRETATION: The annual rate of increase in research output for Canada was more than 3 times higher than that seen world wide. Canada is now ranked seventh among countries contributing human studies to MEDLARS. The increase indicates that Canada's medical schools are productive, competitive in making contributions to medical science and are supporting Canadian journals.
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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 | 0.047 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.123 | 0.156 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".