Multi-centre modified Delphi exercise to identify candidate items for classifying early-stage symptomatic knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To generate a list of candidate items potentially useful for discriminating individuals with Early-stage Symptomatic Knee Osteoarthritis (EsSKOA) from those with other conditions and from established osteoarthritis (OA), and to reduce this list based on expert consensus. DESIGN: We conducted a three-round online international modified Delphi exercise with OA clinicians and researchers ("OA experts"). In Round 1, participants reviewed 84 candidate items and nominated additional item(s) potentially useful for EsSKOA classification; those nominated by ≥3 participants were added. In Round 2, participants rated perceived usefulness of 108 items (1 [not at all useful] to 9 [extremely useful]). In Round 3, participants could revise their ratings after reviewing Round 2 group median and quartiles. Following Round 3, we retained items with a median usefulness score >5 and ≥33.3% of participants categorised the item as useful (7 to 9), overall and in subgroup analysis by clinician field. RESULTS: There were 128 participants in Round 1 and 113 (88%) completed all rounds. We retained 77 items that spanned multiple domains (demographics, symptoms, physical exam, performance-based measures, imaging, laboratory investigations, and gross inspection/arthroscopy). Highly rated items included (median usefulness score): prior knee joint injury (8), diagnosis of OA in a different joint (7), and activity-related knee pain (7). The interquartile range was most often 3. CONCLUSION: We identified 77 items that OA experts consider potentially useful for EsSKOA classification. The results highlight experts' uncertainty around item usefulness. Next, candidate items will be further assessed and reduced using data-driven and multicriteria decision analysis methods.
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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 | 0.126 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".