Improving Identification of Pediatric Feeding Dysfunction Among Registered Dietitian Nutritionists
Bibliographic record
Abstract
All Registered Dietitian Nutritionists (RDN) undergo extensive training to develop the ability to improve dietary intake among individuals of all ages. Treating children (0-18 years of age) is often challenging and requires specialized training. One area that is particularly challenging is identifying children that may not be able to eat appropriately to sustain rapid growth and development. An online survey of RDNs that work specifically with children was conducted to better understand how RDNs are identifying and treating children with feeding problems. From the survey we learned that the methods and procedures used by RDNs for identifying and treating children with feeding problems are variable. A review of current literature identified many tools capable of identifying children at risk for feeding problems. One of these tools was chosen and tested in a population of children 0-3 years of age who were clients of an early intervention program. Use of this tool, the Montreal Children’s Hospital Feeding Scale, increased the number of children that were identified as having feeding dysfunction and who received nutrition services. Implementing feeding dysfunction screening into children’s health care settings would improve the quality of care a child receives and help to improve their overall nutrition status.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".