Pain in the Forecast: Investigating Weather Sensitivity Before and After Total Knee Arthroplasty
Bibliographic record
Abstract
Background: While many patients report that their symptoms are influenced by weather conditions prior to their knee arthroplasty (TKA), how weather-related pain (WRP) evolves following surgery remains poorly understood. This study investigated the prevalence of WRP prior to and after TKA, assessed whether TKA resolves preoperative WRP, evaluated the incidence of new-onset WRP postoperatively, and identified associated risk factors. Methods: We prospectively surveyed 87 patients (121 TKAs) at a mean follow-up of 9 years (range: 1–26 years). Patients completed a standardized questionnaire assessing WRP before and after surgery, along with patient-reported outcome measures (WOMAC, UCLA activity score, and SF-12). Statistical analysis was performed to assess the associations between WRP and clinical or demographic variables, as well as patient-reported outcome measures (PROMs) in patients with and without WRP. Results: Preoperatively, 31% of patients (37/121 knees) reported WRP. Following TKA, WRP resolved in 48% of these cases (18/37 knees), persisted in 16% (19/121 knees), and developed de novo in 16% of patients (20 knees). Postoperative WRP was significantly associated with the presence of WRP in other joints (p < 0.0001), and with female sex (p < 0.0008). Preoperatively, patients with WRP had worse WOMAC scores for pain (p = 0.046), stiffness (p = 0.012), and physical function (p = 0.024). Despite these differences, all groups demonstrated significant improvement in PROMs postoperatively, with no differences between groups at final follow-up (p > 0.125). Conclusions: TKA leads to the resolution of WRP in nearly half of affected patients; however, a subset develops new or persistent WRP. Female sex, and multi-joint involvement are associated with WRP after TKA. These findings underscore the importance of preoperative counseling regarding expectations for pain relief, particularly in relation to weather sensitivity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".