Comparison of a validated decision-support tool to a standard of care triage system for knee osteoarthritis assessment: a proof-of-concept study
Bibliographic record
Abstract
BACKGROUND: Patients with knee osteoarthritis (OA) in northwestern Ontario are referred by their primary care provider (PCP) to a centralized assessment clinic for evaluation by an advanced practice physiotherapist (APP) to determine if they will require surgical management. However, many patients are found to not require surgical management, resulting in delays for patients who do. A decision-support tool was developed to address this issue and to guide treatment options by determining the need for surgical or nonsurgical approaches. METHODS: We used a proof-of-concept method to assess the use of the decision-support tool in northwestern Ontario. Data from 100 consecutive patients assessed for knee OA management were collected from the Thunder Bay centralized assessment clinic. Two levels of agreement analyses (calculated using Cohen κ statistic) were performed, between the APP assessment decision (surgical or non-surgical) and the decision-support tool recommendation, and between the surgeon's decision (surgical or non-surgical) and the decision-support tool recommendation. RESULTS: = 72) between the decision-support tool recommendation and the surgeon's decision. CONCLUSION: The decision-support tool recommendation showed considerable agreement with the decisions of the APP and surgeon indicating that it could be a valuable tool to guide PCPs caring for patients with knee OA. The applicability of a decision-support tool in northwestern Ontario displayed promising results, but further research is needed to examine the feasibility in a primary care setting.
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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 | 0.075 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".