A four-stage evaluation of the Paediatric Yorkhill Malnutrition Score in a tertiary paediatric hospital and a district general hospital
Bibliographic record
Abstract
Paediatric in-patients are at high risk of malnutrition but validated paediatric screening tools suitable for use by nursing staff are scarce. The present study aimed to assess the diagnostic accuracy of the new Paediatric Yorkhill Malnutrition Score (PYMS). During a pilot introduction in a tertiary referral hospital and a district general hospital, two research dietitians assessed the validity of the PYMS by comparing the nursing screening outcome with a full dietetic assessment, anthropometry and body composition measurements. An additional PYMS form was completed by the research dietitians to assess its inter-rater reliability with the nursing staff and for comparison with the Screening Tool for the Assessment of Malnutrition in Paediatrics (STAMP) and the Paediatric Subjective Global Nutritional Assessment (SGNA). Of the 247 children studied, the nurse-rated PYMS identified 59% of those rated at high risk by full dietetic assessment. Of those rated at high risk by the nursing PYMS, 47% were confirmed as high risk on full assessment. The PYMS showed moderate agreement with the full assessment (kappa = 0.46) and inter-rater reliability (kappa = 0.53) with the research dietitians. Children who screened as high risk for malnutrition had significantly lower lean mass index than those at moderate or low risk, but no difference in fat. When completed by the research dietitians, the PYMS showed similar sensitivity to the STAMP, but a higher positive predictive value. The SGNA had higher specificity than the PYMS but much lower sensitivity. The PYMS screening tool is an acceptable screening tool for identifying children at risk of malnutrition without producing unmanageable numbers of false-positive cases.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".