Pedi-R-MAPP: The development of a nutritional awareness tool for use in remote paediatric consultations using a modified Delphi consensus
Bibliographic record
Abstract
BACKGROUND & AIMS: The Remote Malnutrition Application (R-MAPP) was developed during the COVID-19 pandemic to provide support for health care professionals (HCPs) working in the community to complete remote nutritional assessments, and provide practical guidance for nutritional care. The aim of this study was to modify the R-MAPP into a version suitable for children, Pediatric Remote Malnutrition Application (Pedi-R-MAPP), and provide a structured approach to completing a nutrition focused assessment as part of a technology enabled care service (TECS) consultation. METHODS: A ten-step process was completed: 1) permission to modify adult R-MAPP, 2) literature search to inform the Pedi-R-MAPP content, 3) Pedi-R-MAPP draft, 4) international survey of HCP practice using TECS, 5) nutrition experts invited to participate in a modified Delphi process, 6) first stakeholder meeting to agree purpose/draft of the tool, 7) round-one online survey, 8) statements with consensus removed from survey, 9) round-two online survey for statements with no consensus and 10) second stakeholder meeting with finalisation of the Pedi-R-MAPP nutrition awareness tool. RESULTS: The international survey completed by 463 HCPs, 55% paediatricians, 38% dietitians, 7% nurses/others. When HCPs were asked to look back over the last 12 months, dietitians (n = 110) reported that 5.7 ± 10.6 out of every 10 appointments were completed in person; compared to paediatricians (n = 182) who reported 7.5 ± 7.0 out of every 10 appointments to be in person (p < 0.0001), with the remainder completed as TECS consultations. Overall, 74 articles were identified and used to develop the Pedi-R-MAPP which included colour-coded advice using a traffic light system; green, amber, red and purple. Eighteen participants agreed to participate in the Delphi consensus and completed both rounds of the modified Delphi survey. Agreement was reached at the first meeting on the purpose and draft sections of the proposed tool. In round-one of the online survey, 86% (n = 89/104) of statements reached consensus, whereas in round-two 12.5% (n = 13/104) of statements reached no consensus. At the second expert meeting, contested statements were discussed until agreement was reached and the Pedi-R-MAPP could be finalised. CONCLUSION: The Pedi-R-MAPP nutrition awareness tool was developed using a modified Delphi consensus. This tool aims to support the technological transformation fast-tracked by the COVID-19 pandemic by providing a structured approach to completing a remote nutrition focused assessment, as well as identifying the frequency of follow up along with those children who may require in-person assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".