Canadian Medical Education Journal Editorial Medical Education Breakthroughs: What Constitutes
Bibliographic record
Abstract
As a discipline, the medical sciences stand behind a history and foundation of breakthroughs that have lead to some extraordinary advances in medicine. In compiling a list of the greatest breakthroughs since 1840, the British Medical Journal received nominations from 11,362 readers in identifying the top 15 most important.1 The decision was understandably difficult. If it is based on the number of lives saved then vaccines would have to be one of the top choices. If societal consequences are of major importance, however, then the introduction of the pill as a symbol of women’s contraceptive autonomy is undeniable.2 In the end, sanitation received the greatest number of votes as infectious diseases during the 19th century resulted in inexplicable rates of morbidity and mortality. As explained by Mackenbach, the consequences of economic growth through industrialization and international trade and transport resulted in overcrowding and the spread of infectious diseases such as smallpox, tuberculosis, diphtheria, measles, typhoid, cholera and flu viruses.3 To this day, improvement in clean water systems and sewage disposal have been credited with saving millions of lives, and sanitation is view as a standard of how best to improve public and population health. In a recent book titled Breakthrough! How the 10
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.068 | 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".