A new decision tree for diagnosis of osteoarthritis in primary care: international consensus of experts
Bibliographic record
Abstract
Although osteoarthritis (OA) is managed mainly in primary care, general practitioners (GPs) are not always trained in its diagnosis, which leads to diagnostic delays, unnecessary resource utilization, and suboptimal patient outcomes.To address this situation, an International Rheumatologic Board (IRB) of 8 experts from 3 continents developed guidelines for the diagnosis of OA in primary care. The focus was three major topologies: hip, knee, and hand/finger OA. The IRB used American College of Rheumatology diagnostic criteria.Care pathways based on clinical and radiological findings were developed to identify intervention thresholds for GPs/specialists. To optimize usefulness in the primary care setting, the guidelines were formatted as an uncomplicated, but comprehensive one-page decision tree for each topology, highlighting key aspects of the evaluation process and incorporating red flags. In a two-phase validation stage, the draft guidelines were evaluated by rheumatologists and GPs for project execution, content and perceived benefit. The strength of the guidelines lies in their user-friendly diagram and potential for broad application. Such guidelines will allow GPs to make an easy but definite diagnosis of OA and offer clear guidance about situations requiring an expert opinion. The guidelines have potential to improve patient outcomes and reduce the number of unnecessary procedures.This project demonstrated the feasibility of developing easy-to-use and effective visual decision trees to facilitate the diagnosis and management of OA of the hip, knee and hand/finger in primary care. The next step should be to conduct a large impact study of implementation of these recommendations in the diagnostic management of OA in general practice in different areas.
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 | 0.067 | 0.179 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".