The RCS England COVID-19 Surgical Research Group: early findings and lessons to influence surgical practice.
Bibliographic record
Abstract
INTRODUCTION: Surgeons and allied professionals have been quick to respond to the need for evidence during the COVID-19 pandemic. The Royal College of Surgeons of England (RCS England) has provided formal recognition, support and guidance to all members of its interdisciplinary collaborative COVID Research Group (RCS CRG). We describe research conducted by members of this group, initial findings and lessons for clinical practice so far. METHODS: Members of the more than 50 projects included so far in the RCS CRG portfolio were invited to provide a summary of their project and findings to date. The 26 summaries received were collated and broad themes identified to produce this summary document. RESULTS: Wide-ranging projects have been conducted by members of the RCS CRG, rapidly yielding crucial insights into the behaviour of the SARS-CoV-2 pathogen, its impact on patients and staff, the challenges it presents to surgical practice and investigation into methods to adapt and overcome such challenges. CONCLUSIONS: The response of the surgical research community to COVID-19 has been rapid and well-organised. Early establishment of a formal network under the auspices of RCS England has assisted efficient research collaboration and delivery, while avoiding academic duplication between groups. This has led to a high research output, directly informing and substantially influencing practice throughout and beyond the 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 | 0.067 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".