COVID-19: lessons learnt and priorities in trauma and orthopaedic surgery
Bibliographic record
Abstract
The COVID-19 pandemic is the most serious health crisis of our time. Global public measures have been enacted to try to prevent healthcare systems from being overwhelmed. The trauma and orthopaedic (T&O) community has overcome challenges in order to continue to deliver acute trauma care to patients and plan for challenges ahead. This review explores the lessons learnt, the priorities and the controversies that the T&O community has faced during the crisis. Historically, the experience of major incidents in T&O has focused on mass casualty events. The current pandemic requires a different approach to resource management in order to create a long-term, system-sustaining model of care alongside a move towards resource balancing and facilitation. Significant limitations in theatre access, anaesthetists and bed capacity have necessitated adaptation. Strategic changes to trauma networks and risk mitigation allowed for ongoing surgical treatment of trauma. Outpatient care was reformed with the uptake of technology. The return to elective surgery requires careful planning, restructuring of elective pathways and risk management. Despite the hope that mass vaccination will lift the pressure on bed capacity and on bleak economic forecasts, the orthopaedic community must readjust its focus to meet the challenge of huge backlogs in elective caseloads before looking to the future with a robust strategy of integrated resilient pathways. The pandemic will provide the impetus for research that defines essential interventions and facilitates the implementation of strategies to overcome current barriers and to prepare for future crises.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".