Canadian Nationwide Survey on Pediatric Malnutrition Management in Tertiary Hospitals
Bibliographic record
Abstract
Background: Disease-associated malnutrition (DAM) is common in hospitalized children. This survey aimed to assess current in-hospital practices for clinical care of pediatric DAM in Canada. Methods: An electronic survey was sent to all 15 tertiary pediatric hospitals in Canada and addressed all pillars of malnutrition care: screening, assessment, treatment, monitoring and follow-up. Results: Responses of 120 health care professionals were used from all 15 hospitals; 57.5% were medical doctors (MDs), 26.7% registered dietitians (RDs) and 15.8% nurses (RNs). An overarching protocol for prevention, detection and intervention of pediatric malnutrition was present or “a work in progress”, according to 9.6% of respondents. Routine nutritional screening on admission was sometimes or always performed, according to 58.8%, although the modality differed among hospitals and profession. For children with poor nutritional status, lack of nutritional follow-up after discharge was reported by 48.5%. Conclusions: The presence of a standardized protocol for the clinical assessment and management of DAM is uncommon in pediatric tertiary care hospitals in Canada. Routine nutritional screening upon admission has not been widely adopted. Moreover, ongoing nutritional care of malnourished children after discharge seems cumbersome. These findings call for the adoption and implementation of a uniform clinical care pathway for malnutrition among pediatric hospitals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".