Musculoskeletal Oncology: Patient Triage and Management during the COVID-19 Pandemic
Bibliographic record
Abstract
Sarcoma treatment during the covid-19 pandemic is a new challenge. This patient population is often immunocompromised and potentially more susceptible to viral complications. Government guidelines highlight the need to minimize patient exposure to unnecessary hospital visits. However, those guidelines lack practical recommendations on ways to manage triage and diagnosis expressly for new cancer patients. Furthermore, there are no reports on the efficiency of the guidelines. One of the main issues in treating musculoskeletal tumours is the complexity and variability of presentation. We offer a triage model, used in a quaternary-referral musculoskeletal oncology centre, that allows us to maintain an open pathway for referral of new patients while minimizing exposure risks. A multidisciplinary approach and analysis of existing investigations allow for a pre-clinic evaluation. The model identifies 3 groups of patients: ■ Patients with suspected high-grade malignancy, or benign cases with aggressive features, both in need of further evaluation in the clinic and prompt treatment■ Patients with low-grade malignancy, and benign cases whose treatment is not urgent, that are managed during the pandemic by telemedicine, with reassurance and information about their illness■ Patients who can be managed by their local medical professionals In comparison to a pre-pandemic period, that approach resulted in a higher ratio of malignant-to-benign conditions for new patients seen in the clinic (3:4 vs. 1:3 respectively), thus using available resources more efficiently and prioritizing patients with suspected high-grade malignancy. We believe that this triage system could be applied in other surgical oncology fields during a pandemic.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".