<i>Méta-analyse en médecine</i> : the first book on systematic reviews in medicine
Bibliographic record
Abstract
Interest in systematic reviews and meta-analysis in medicine began in the late 1970s.[1][2][3][4][5][6] During the 1980s, these methods began to be adopted more widely by medical researchers, and in the late 1980s, expository journal articles began to appear, [7][8][9][10][11] and the first book about meta-analysis in medicine was published.12 The book was published in 1987 by Milos Jenicek, a professor at the Universite´de Montre´al.Too often, the anglophone world remains unaware of important contributions to science and other fields which have been published in languages other than English.So it was with this book, which was published in French.A bilingual friend -Michael Kramer, a professor of epidemiology at McGill University in Montre´al -obtained a copy of the book for me in 1994.After reading and greatly enjoying it, I visited Montre´al in October of that year and asked Milos to sign my copy.He wrote: 'To Dr Iain Chalmers
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.015 |
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".