WEATHERING THE STORM: THE IMPACT OF TOTAL HIP ARTHROPLASTY ON WEATHER-RELATED PAIN
Bibliographic record
Abstract
It is not unusual for patients prior to their total hip arthroplasty (THA) to indicate that their symptoms worsen with certain meteorological conditions. However, the prevalence and evolution of weather-related pain (WRP) following THA remain poorly understood. The aim of this study was to investigate the prevalence of WRP both before and after primary THA, to assess the impact of THA on pre-existing WRP, and to identify potential risk factors associated with WRP. An in-person survey was conducted on 442 primary THAs, in 327 consecutive patients at the time of their postoperative follow-up. Each patient was assessed using a questionnaire specifically designed to address weather-related joint pain before and after their THA. The clinical evaluation included patient-reported outcome scores (PROMS). Preoperatively, 17% of THA hips experienced WRP, with 71% achieving complete resolution postoperatively (p < 0.001). In patients with WRP in multiple joints and their hip, the THA failed to alleviate the WRP in 31% of the cases. In addition, 8% of the THA patients developed de-novo WRP after surgery. Although the overall prevalence of WRP in hips post-THA was 12%, the prevalence was 29% in hips with WRP preoperatively and 31% in the hips of patients with WRP in multiple joints as well as their hip. WRP in other joints, the initial diagnosis, body mass index (BMI), and age were correlated with WRP following THA. WRP is not uncommon prior to and after THA. Although THA can effectively alleviate WRP in specific patient populations, it does not universally eliminate preoperative WRP or prevent the emergence of new WRP after surgery.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".