Commentary: Breaking the Mold of Normative Clinical Decision Making: Is It Adaptive, Suboptimal, or Somewhere in Between?
Bibliographic record
Abstract
UNLABELLED: Two articles in this issue, "Contextual decision making and the implementation of clinical guidelines" by Falzer and Garman, and " PERSPECTIVE: Uses and misuses of thresholds in diagnostic decision making" by Warner et al, take very different approaches to the issue of variation among physicians in diagnostic and therapeutic decision making. Falzer and Garman critically examine the well-known phenomenon of poor compliance with practice guidelines. They view this as a reflection of the mismatch between the guideline and the characteristics and needs of individual patients, and as a consequence of the adaptive judgment of the physician. They go on to show that, as the match between the individual patient and the hypothetical patient in the guideline increases, adherence with guidelines increases. Warner and colleagues take a more theoretical position on the larger issue of physician decision making, through the concept of "decision thresholds" originally advanced by Pauker and Kassirer, and attempt to model an example of clinical judgments through this conceptualization. The two articles represent an intriguing contrast on many levels, most fundamentally in that the first views any departure from normative behavior as goal oriented and adaptive and seeks to understand it; the second views it as suboptimal and seeks to minimize it.
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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.085 | 0.059 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".