Evidence-Based Medicine, Best Practices, Transductive Models, and Naturalistic Decision Making: Commentary on Paul R. Falzer, Naturalistic Decision Making and the Practice of Health Care
Bibliographic record
Abstract
Expert and informed decision making is an essential process in all of health care. Evidence-Based Medicine (EBM) purports to support and enhance this process by the timely infusion of high-quality, pertinent evidence from health research, tailored as closely as possible to the individual and their health problem. Doing so is not an easy task for many reasons, beginning with imperfections and incompleteness in the evidence and ending with the complexities of the dual decision making required by individuals and their care providers. EBM needs a lot of help supporting decision-making processes and welcomes further interdisciplinary collaboration. The “conformist principle,” “best practice regimens,” and “transductive models” should not be considered as barriers to such collaboration: These are not part of EBM. Rather, EBM has always seen evidence from health research as but one of many inputs to decision making by providers and patients. An overarching problem for collaboration to address is understanding the decision-making process well enough to develop effective means to bolster it, so that people are consistently offered the current best options for their problems in a way that fits their circumstances and that they can understand and judge.
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 | 0.036 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.012 | 0.005 |
| Research integrity | 0.042 | 0.068 |
| Insufficient payload (model declined to judge) | 0.004 | 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".