VOICES FROM THE FIELD- Pediatric Feeding Disorders: The View from One Clinical Setting
Bibliographic record
Abstract
The CEECD reviews1-5 on eating behaviour in young children, taken collectively, do an excellent job of covering the breadth of issues in this field. They confirm the progress that has been made over the last two decades in raising general awareness of the importance of eating behaviour in young children as a health concern and the progress that has been made in the biopsychosocial6 framework that underpins current treatments when problems arise. They also highlight nicely the challenges that face the field. Benoit’s caution that the field of feeding problems in infants and young children is still plagued by “inconsistent definitions, differing and essentially non-validated diagnostic and conceptual frameworks and inconsistent methodologies”1 should be kept in mind by anyone reading the literature or working in this area. This problem is particularly evident in discussions on the general incidence of feeding problems in which the data cited are typically 20 to 25 years old and the research was conducted with essentially non-validated and differing definitions. It is understandable that investigators do this because at the current time there is simply no better information. Canadian pediatric tertiary-care centres have responsibility for the health of the infants,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.018 | 0.035 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.039 | 0.069 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".