Development of the Pediatric Integrated Nutrition Pathway for Acute Care (P-INPAC) using a modified Delphi technique
Bibliographic record
Abstract
One in three hospitalized children have disease-related malnutrition (DRM) upon admission to hospital, and all children are at risk for further nutritional deterioration during hospital stay; however, systematic approaches to detect DRM in Canada are lacking. To standardise and improve hospital care, the multidisciplinary pediatric working group of the Canadian Malnutrition Taskforce aimed to develop a pediatric, inpatient nutritional care pathway based on available evidence, feasibility of resources, and expert consensus. The working group ( n = 13) undertook a total of four meetings: an in-person meeting to draft the pathway based on existing literature and modelled after the Integrated Nutrition Pathway for Acute Care (INPAC) in adults, followed by three online surveys and three rounds of online Delphi consensus meetings to achieve agreement on the draft pathway. In the first Delphi survey, 32 questions were asked, whereas in the second and third rounds 27 and 8 questions were asked, respectively. Consensus was defined as any question/issue in which at least 80% agreed. The modified Delphi process allowed the development of an evidence-informed, consensus-based pathway for inpatients, the Pediatric Integrated Nutrition Pathway for Acute Care (P-INPAC). It includes screening <24 h of admission, assessment with use of Subjective Global Nutritional Assessment (SGNA) <48 h of admission, as well as prevention, and treatment of DRM divided into standard, advanced, and specialized nutrition care plans. Research is necessary to explore feasibility of implementation and evaluate the effectiveness by integrating P-INPAC into clinical practice.
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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 | 0.105 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".