Bibliographic record
Abstract
Academic medicine, in its broadest sense, has made major contributions to human health in the past quarter century. This has been achieved in large part because it has attracted an outstanding cadre of--largely altruistic--professionals. These pioneering efforts have served as the life-blood of the discipline. Their journeys of discovery, often complemented by collaboration with the pharmaceutical, biotechnological and device industry have yielded remarkable insights into the diagnosis, treatment and prevention of disease and been celebrated by a stunning array of Nobel laureates in medicine and related arenas of endeavour.1 The translation of discovery to the bedside, clinic and the community coupled, most recently, with insights into the gap between potential effectiveness and what ultimately occurs as part of health care delivery, have been monumental in scope. This progress has unquestionably been the province of the university based clinician scientist. Within Canada, the emergence of the Canadian Institutes of Health Research, the Canadian Foundation for Innovation, and the Canada Research Chairs has been pivotal in launching the careers of a new generation of clinician scientists. The excitement of discovery, gratification associated with direct patient care, and satisfaction of inspiring learning while engaging the next generation of emerging health professionals is rewarded by a career in academic medicine characterized by extraordinary challenge, fulfillment and meaning. As remarkable as these advances in quantity and quality of life have been (in large part attributable to health care research and its implementation) the promises of molecular medicine and abundant new technologies portend an exciting future whereby academic medicine can build upon its noble and traditional contributions to human health.
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 | 0.068 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.039 |
| Scholarly communication | 0.035 | 0.030 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.020 | 0.060 |
| Insufficient payload (model declined to judge) | 0.033 | 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".