A survey of the workload generated by older surgical patients referred to on-call medical registrars—SNAP-3
Bibliographic record
Abstract
Background: Older surgical patients who develop medical problems are commonly referred to medical teams, which can be proactive physician-led teams or through reactive referral to the on-call medical registrar. Methods: A cross-sectional survey of on-call medical registrars who received referrals from surgical teams was conducted in March-June 2022 at 140 NHS hospitals. It focused on the workload derived from referrals of older surgical patients to on-call medical registrars, excluding referrals to existing services such as perioperative medicine, orthogeriatric, or medical specialty teams. To minimise recall bias, completion of the survey was encouraged regardless of whether a registrar had received a referral. The aim of this survey was to estimate the unplanned, acute workload generated by older surgical patients requiring referral to on-call medical registrars. The survey also aimed to estimate the prevalence and nature of training in perioperative medicine amongst medical registrars. Results: During an on-call shift, 41.3% (266/644) of medical registrars received at least one referral regarding an older surgical patient. The commonest indications were arrhythmia, acute respiratory problems, electrolyte abnormalities, suspected myocardial infarction, sepsis, and delirium. Three-quarters of registrars reported not receiving training in perioperative management of older patients. Conclusions: The findings highlight the significant workload and training gaps faced by medical registrars in managing older surgical patients. Bridging the gap between national recommendations and local services may reduce demands on on-call registrars and improve care.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".