How Musculoskeletal Tumor Management Changed During the COVID-19 Pandemic: Data from a Nationwide Questionnaire Survey of Hospitals Specializing in Musculoskeletal Tumors in Japan
Bibliographic record
Abstract
BACKGROUND: While changes in clinical practice during the COVID-19 pandemic in Japan have been widely studied, data specific to bone and soft tissue tumor care remain limited. METHODS: A nationwide web-based survey was conducted among hospitals specializing in musculoskeletal tumors. It assessed the occurrence of COVID-19-related events (patient infections, outbreak clusters, and staff infections), delays in referral and diagnosis, postponement or cancellation of specific treatments, and changes in institutional management strategies. RESULTS: Seventy-eight hospitals (91.7% of all specialized centers) responded. Patient infections, outbreak clusters, and staff infections were reported by 28.2%, 48.7%, and 53.8% of hospitals, respectively. While radiological exams and biopsies were largely maintained, patient referrals decreased significantly. Surgical treatment was more affected than chemotherapy or radiotherapy. Strategy changes included surgery delays or cancellations (48.7%) and prolonged follow-up intervals (20.5%). Among COVID-19-related factors, only direct patient infections were significantly associated with institutional changes in treatment policy. CONCLUSIONS: The pandemic substantially disrupted outpatient services and surgical care in musculoskeletal oncology. Patient infection was the main driver of treatment strategy modifications.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".