Bibliographic record
Abstract
As the preeminent Canadian physician Sir William Osler so eloquently put it, Medicine is a science of uncertainty and an art of probability. Clinicians often forecast the benefits of specific courses of action when consulting with patients, yet the reliability of the underpinning evidence, inherent biases (conscious and unconscious), and the limitations of medical findings often lead to diagnostic and/or treatment errors. In this paper, the authors present a number of specific examples related to risk and uncertainty in the context of clinical decision making-some more than a little alarming-including extremely high incidences of misdiagnosis, reluctance on the part of medical professionals to abandon treatment regimens that are doing patients no good and may be causing harm, and systemic flaws in medical research methodology that can impede important new data from reaching practitioners. The article concludes by exploring ways medical practice could change to reduce health risks, uncertainty, and errors. Copyright International Institute of Forecasters, 2019
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 | 0.013 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.038 | 0.041 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".