Identifying pain susceptibility phenotypes in knee osteoarthritis.
Bibliographic record
Abstract
Knee pain in osteoarthritis is complex and complicated by the fact that osteoarthritis is considered to be a disorder of multiple phenotypes. This complexity challenges our understanding as to why some people remain relatively symptom-free, while others progress to persistent pain. One approach to understanding the mechanisms underlying the transition to persistent pain is by identifying pain susceptibility phenotypes in people with or at risk of knee osteoarthritis. Using variables representative of the multidimensional nature of pain in people who were free of persistent pain, we identified four phenotypes characterised by low pressure pain thresholds and temporal summation and not psychosocial factors in those who developed persistent pain two years later. The group with the highest proportion of low pressure pain thresholds and a moderate proportion with facilitated temporal summation had twice the odds of developing persistent knee pain. This work provides preliminary insights into the critical importance of altered neurobiological mechanisms of pain signalling that contributes to development of chronic, persistent pain in knee osteoarthritis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".