Management of common elective paediatric orthopaedic conditions during the COVID-19 pandemic: The Montreal experience
Bibliographic record
Abstract
PURPOSE: To explore safe delays for the treatment of common paediatric orthopaedic conditions when faced with a life-threatening pandemic, COVID-19, and to propose a categorization system to address this question. METHODS: Review of the literature related to acceptable delays for treatment of common orthopaedic conditions, experience of healthcare professionals from low resource communities and expertise of experienced surgeons. RESULTS: Guidelines for the management of cancellations of elective surgeries during a period of resource reallocation are proposed. Elective cases must not be postponed indefinitely as adverse outcomes may result. Triage of waiting lists should include continuous monitoring of the patient and close communication with families despite social distancing and travel restrictions. Telehealth becomes a necessity. Common orthopaedic conditions are triaged into four groups according to urgency and safe and acceptable delay. Categories proposed are Emergent (life and limb threatening conditions), Urgent (within seven days), Semi-elective (postponed for three months) and Elective (postponed for three to 12 months). In total, 25 common orthopaedic conditions are reviewed and categorized. CONCLUSION: Given the uncertainty within healthcare during a pandemic, it is necessary to determine acceptable delays for elective conditions. We report our experience in developing guidelines and propose categorizing elective cases into four categories, based on the length of delay. Telemedicine plays a key role in determining the gravity of each situation and hence the amount of delay. These guidelines will assist others dealing with elective cases in the midst of a crisis. This paper initiates a coordinated effort to develop a consensus statement on safe delays.Published without peer review.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".