Frailty and emergency surgery: identification and evidence‐based care for vulnerable older adults
Bibliographic record
Abstract
Frailty is a multidimensional state related to accumulation of age- and disease-related deficits across multiple domains. Older people represent the fastest growing segment of the peri-operative population, and 25-50% of older surgical patients live with frailty. When frailty is present before surgery, adjusted rates of morbidity and mortality increase at least two-fold; the odds of delirium and loss of independence are increased more than four- and five-fold, respectively. Care of the older person with frailty presenting for emergency surgery requires individualised and evidence-based care given the high-risk and complex nature of their presentations. Before surgery, frailty should be assessed using a multidimensional frailty instrument (most likely the Clinical Frailty Scale), and all members of the peri-operative team should be aware of each patient's frailty status. When frailty is present, pre-operative care should focus on documenting and communicating individualised risk, considering advanced care directives and engaging shared decision-making when feasible. Shared multidisciplinary care should be initiated. Peri-operatively, analgesia that avoids polypharmacy should be provided, along with delirium prevention strategies and consideration of postoperative care in a monitored environment. After the acute surgical episode, transition out of hospital requires that adequate support be in place, along with clear discharge instructions, and review of new and existing prescription medications. Advanced care directives should be reviewed or initiated in case of readmission. Overall, substantial knowledge gaps about the optimal peri-operative care of older people with frailty must be addressed through robust, patient-oriented research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".