22 Design and Implementation of A Nutrition Clinical Pathway for Patients with Fractured Neck of Femur
Bibliographic record
Abstract
Abstract Background Patients with a fractured neck of femur (FNOF) are commonly malnourished pre-admission, have reduced oral intake in hospital and a hypermetabolic state up to three months postoperatively (E Paillaud 2000). Malnutrition is associated with functional deterioration, higher morbidity and mortality. Evidence suggests nutritional supplementation post-surgery can reduce postoperative complications. As a result, nutritional assessment is included in the National Hip Fracture Database best practice tariff (Avenell, Cochrane Database of Systematic Reviews 2016). Introduction Our aim was to design and implement a clinical pathway for patients with FNOF to identify malnutrition and provide appropriate nutritional support. Intervention A retrospective audit of 25 patients was completed to understand baseline rates of assessment, prescription of supplements and referral to dietetics. Using these data meetings were arranged to develop a clinical pathway. Key stakeholders included dietetics, orthopaedic surgeons, geriatricians, physiotherapists and nurses. The pathway was evaluated and optimised with two Plan-Do-Study-Act (PDSA) cycles looking at 25 patients each time. Results Baseline: 79% received a nutritional assessment, 32% had nutritional supplements prescribed and 36% (n=9) met criteria for referral to a dietician, of which 55%were referred. However, an additional 5 referrals were made to dietetics for patients who did not meet criteria, a 50% inappropriate referral rate. PDSA cycle 1: increased nutritional assessment (85%), increased nutritional supplements prescribed (92%), decreased inappropriate referrals to dietetics (43%). PDSA cycle 2: increased nutritional assessment & nutritional supplements prescribed (100%), increased inappropriate referrals to dietetics (80%). Conclusions The implementation of a nutrition pathway has led to increased identification and treatment of malnutrition, which has in addition improved accrual of the best practice tariff. However, greater number of inappropriate referrals have been made to dietetics. This is partly attributed to difficulty weighing patients on admission, and where no weight is inputted on the Malnutrition Universal Screening Tool a “High Risk” score is generated triggering a referral. We are now looking at alternative methods to obtaining a weight such a mid-upper arm circumference.
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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 | 0.042 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".