Development of a Soft Tissue Knee Clinical Decision-Making Tool for Patients Presenting to Primary Point-of-Care Providers in Alberta, Canada
Bibliographic record
Abstract
Several barriers exist in Alberta, Canada to providing accurate and accessible diagnoses for patients presenting with acute knee injuries and chronic knee problems. In efforts to improve quality of care for these patients, an evidence-informed clinical decision-making tool was developed. Forty-five expert panelists were purposively chosen to represent stakeholder groups, various expertise, and each of Alberta Health Services' 5 geographical health regions. A systematic rapid review and modified Delphi approach were executed with the intention of developing standardized clinical decision-making processes for acute knee injuries, atraumatic/overuse conditions, knee arthritis, and degenerative meniscus. Standardized criteria for screening, history-taking, physical examination, diagnostic imaging, timelines, and treatment were developed. This tool standardizes and optimizes assessment and diagnosis of acute knee injuries and chronic knee problems in Alberta. This project was a highly collaborative, province-wide effort led by Alberta Health Services' Bone and Joint Health Strategic Clinical Network (BJH SCN) and the Alberta Bone and Joint Health Institute (ABJHI).
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 | 0.044 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".