Judgement and Decision Making in Clinical and Return-to-Sports Decision Making: A Narrative Review
Bibliographic record
Abstract
Making return-to-sport decisions can be complex and multi-faceted, as it requires an evaluation of an individual's physical, psychological, and social well-being. Specifically, the timing of progression, regression, or return to sport can be difficult to determine due to the multitude of information that needs to be considered by clinicians. With the advent of new sports technology, the increasing volume of data poses a challenge to clinicians in effectively processing and utilising it to enhance the quality of their decisions. To gain a deeper understanding of the mechanisms underlying human decision making and associated biases, this narrative review provides a brief overview of different decision-making models that are relevant to sports rehabilitation settings. Accordingly, decisions can be made intuitively, analytically, and/or with heuristics. This narrative review demonstrates how the decision-making models can be applied in the context of return-to-sport decisions and shed light on strategies that may help clinicians improve decision quality.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".