Barriers to nutritional intake in patients with acute hip fracture: time to treat malnutrition as a disease and food as a medicine?
Bibliographic record
Abstract
Inadequate energy and protein intake leads to malnutrition; a clinical disease not without consequence post acute hip fracture. Data detailing malnutrition prevalence, incidence, and intake adequacy varies widely in this patient population. The limited success of reported interventional strategies may result from poorly defined diagnostic criteria, failure to address root causes of inadequate intake, or errors associated with selection bias. This pragmatic study used a sequential, explanatory mixed methods design to identify malnutrition aetiology, prevalence, incidence, intake adequacy, and barriers to intake in a representative sample of 44 acute hip fracture patients (73% female; mean age, 81.7 ± 10.8 years). On admission, malnutrition prevalence was 52.2%. Energy and protein requirements were only met twice in 58 weighed 24 h food records. Mean daily patient energy intake was 2957 kJ (50.9 ± 36.1 kJ·kg(-1)) and mean protein intake was 22.8 g (0.6 ± 0.46 g·kg(-1)). This contributed to a further in-patient malnutrition incidence of 11%. Barriers to intake included patient perceptions that malnutrition and (or) inadequate intake were not a problem, as well as patient and clinician perceptions that treatment for malnutrition was not a priority. Malnutrition needs to be treated as a disease not without consequence, and food should be considered as a medicine after acute hip fracture.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".