Does moderate or severe nonspecific knee injury affect radiographic osteoarthritis incidence and progression?
Bibliographic record
Abstract
BACKGROUND: Knee injuries can lead to radiographic osteoarthritis (ROA). Injuries may be "specific" (SI) including ligament or meniscal tears or patellar trauma, or "nonspecific" (NSI). Our objective is to understand the effect of knee NSI on ROA incidence and progression. METHODS: 163 people (sample-weighted for population representativeness) aged 40+ with history of knee pain had radiographs assessed on Kellgren Lawrence (KL) grade (0/1 collapsed) at baseline and follow-up (median 3.2 years apart). Progression was an increase in KL score. SIs and NSIs were labeled "severe" (walking aid for ≥1 week) or "moderate". One model treated SI and NSI as dichotomous (yes/no), and another as trichotomous (none/moderate/severe). Models were adjusted for age, sex, BMI, KL grade and follow-up time. RESULTS: SI/NSI history was none, moderate (7.8/24.4%) or severe (11.0/10.8%). Duration at baseline since SI/NSI ranged from <1 year to several decades (SI/NSI mean 4.6/6.5 years). SI was significantly associated with ROA incidence and progression (odds ratio (OR) = 2.90; 95% CI = 1.04, 8.09), but NSI showed no significant effect (OR = 1.36; 95% CI = 0.61, 3.02). In the trichotomous model, severe SI was significant (OR = 4.35, 95% CI = 1.26, 15.02), while moderate SI was not (OR = 1.51, 95% CI = 0.33, 6.84). NSI showed no effect: moderate OR = 1.51, 95% CI = 0.61, 3.74; severe OR = 0.90, 95% CI = 0.24, 3.40. This study had 80% power to detect an NSI OR of 2.9. CONCLUSION: We find no evidence that history of NSI affects knee ROA incidence and progression in a population with knee pain, adjusting for SI, age, sex, BMI, KL grade and follow-up time.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".