Understanding Hip Pain Through Social Media: An Initial Overview of an International Web-Based Survey
Bibliographic record
Abstract
Background: We aimed to understand the adult experience of hip pain through a web-based REDCap platform via social media. The purpose of this study was to assess the possibility of collecting patient-reported data through social media in people with hip pain while outlining the contents of the survey and analyzing the demographics of the sample population. Methods: The survey link was active from October 1, 2023, to May 1, 2024, and available on social media platforms. Respondents provided consent prior to survey participation. Responses were anonymous, and only unique, fully complete surveys were analyzed. The comprehensive hip survey included demographic and overall health reporting, as well as hip-specific diagnoses, hip-specific functional measures, and mental health outcomes. Results: Six hundred twenty-seven surveys were initiated, with 509 surveys completed. Twenty-six countries were represented with most responses originating from the United States (72.1%, n = 367), United Kingdom (10%, n = 51), Canada (5.5%, n = 28), and Australia (4.1%, n = 21). Ninety-three percent of respondents were women, with a mean age of 39 (range: 18-77). Top diagnoses reported were hip dysplasia (60.9%, n = 310), femoroacetabular impingement syndrome (45.2%, n = 230), Perthes disease (6.4%, n = 33), and osteoarthritis (6.3%, n = 32). Seventy-one percent (n = 366) reported previous hip surgery, with hip arthroscopy (60.7%, n = 222), periacetabular osteotomy (50.3%, n = 184), and total hip arthroplasty (24.3%, n = 89) being the most reported procedures. Conclusions: This study demonstrates the feasibility of utilizing social media for a comprehensive web-based survey to gather patient-reported outcomes from individuals with various sources of hip pain internationally.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".