Pigmented villonodular synovitis: a crowdsourcing study of two hundred and seventy two patients
Bibliographic record
Abstract
PURPOSE: We aimed to ascertain the feasibility of crowdsourcing via Facebook for medical research purposes; by investigating surgical, oncological and functional outcome and quality-of-life (QOL) in patients with pigmented villonodular synovitis (PVNS) enrolled in a Facebook community (1112 members). METHODS: Patients completed online open surveys on demographics, surgery and clinical outcomes (group 1); and patient-reported outcome measures (PROMs) including knee-injury osteoarthritis outcome score (KOOS), hip-disability osteoarthritis outcome score (HOOS), Toronto extremity salvage score (TESS) and SF-36 (group 2). Mean follow-up was 70 months (12-374). Consistency checks were performed with Cohen's kappa statistic for intra-rater agreement. RESULTS: The first survey was completed by 272 patients (group 1) and 72 patients completed the second (group 2). In group 1, recurrence-rate was 58 % (69/118) after arthroscopic, 36 % (35/97) after open and 50 % (5/10) after combined synovectomy (p = 0.003). In group 2, recurrence-rate was 67 % (26/39) after arthroscopic and 51 % (17/33) after open synovectomy (p = 0.19). Recurrence-risk was increased for diffuse disease (OR = 16; 95%CI = 3.2-85; p < 0.001). Mean function and QOL did not differ after arthroscopic or open synovectomy: KOOS 49 vs. 58 (p = 0.24), HOOS 62 vs. 53 (p = 0.56), TESS 78 vs. 82 (p = 0.86), SF-36 61 vs. 66 (p = 0.41). Cohen's kappa statistic for intra-rater agreement was good to outstanding (κ = 0.68-0.95; p < 0.001). CONCLUSION: Local recurrence-risk was higher for diffuse-type disease and arthroscopic synovectomy. Functional outcome and QOL were comparable for both types of surgery. Gathering data via crowdsourcing seems a promising and innovative way of evaluating rare diseases including PVNS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".