10INTRODUCING NURSE-LED PROCEDURE CASE FINDING IN OLDER EMERGENCY GENERAL SURGICAL (EGS) PATIENTS
Bibliographic record
Abstract
Recent reports recommend early assessment and intervention by geriatricians to improve outcomes for older surgical patients. However, these recommendations are difficult to put into practice, with few units currently providing proactive care for these patients. Translation has been hampered by a reliance on traditional models of care, insufficient workforce/funding and a lack of guidance on how to identify patients requiring geriatrician input. Older EGS patients referred to geriatric medicine inconsistently and reactively often late in pathway, when complications established. Could proactive case-finding and management in EGS be nurse-led and integrated into existing liaison services? Proactive early case-finding led by part-time Advanced Nurse Practitioner (ANP). Patients identified through electronic registration tagging, attendance at EGS handover and twice weekly MDT board rounds. Identified patients underwent CGA. Data collection on consecutive patients over twelve weeks. Consistent identification of older EGS patients requiring CGA (n89) Proactive case-finding using a structured approach can be completed by an ANP in 45 minutes per day. CGA resulted in clear reporting of multimorbidty and frailty* 82% ≥2 conditions, 43% ≥5 conditions Frailty*, 52% of patients. Development of interdepartmental communication Consistent National Emergency Laparotomy Audit (NELA) data entry. Clearer understanding that; Majority of older EGS patients don't have surgery, 54%. 24%, invasive surgery. 21%, diagnostic procedure only. All deaths occurred in ‘frail’ patients. Pathways to identify and case manage older surgical patient can be established and delivered by an ANP. This may be an alternative to traditional geriatrician-led services. Recognition is made that pre-existing elective surgical geriatric liaison service possibly made integration of service change easier. Further study recommended. Comparison of reported frailty against a validated frailty assessment tool Impact of early CGA on patient outcomes. *Frailty phenotype reported by junior medical staff
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".