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Record W2040039687 · doi:10.1542/peds.2014-1630

A Practical Approach to Classifying and Managing Feeding Difficulties

2015· review· en· W2040039687 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePEDIATRICS · 2015
Typereview
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsNorth York General HospitalUniversity of Toronto
FundersAbbott Laboratories
KeywordsMedicineCategorizationVariety (cybernetics)Set (abstract data type)Developmental psychologyClinical psychologyPsychology

Abstract

fetched live from OpenAlex

Many young children are thought by their parents to eat poorly. Although the majority of these children are mildly affected, a small percentage have a serious feeding disorder. Nevertheless, even mildly affected children whose anxious parents adopt inappropriate feeding practices may experience consequences. Therefore, pediatricians must take all parental concerns seriously and offer appropriate guidance. This requires a workable classification of feeding problems and a systematic approach. The classification and approach we describe incorporate more recent considerations by specialists, both medical and psychological. In our model, children are categorized under the 3 principal eating behaviors that concern parents: limited appetite, selective intake, and fear of feeding. Each category includes a range from normal (misperceived) to severe (behavioral and organic). The feeding styles of caregivers (responsive, controlling, indulgent, and neglectful) are also incorporated. The objective is to allow the physician to efficiently sort out the wide variety of conditions, categorize them for therapy, and where necessary refer to specialists in the field.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.191
GPT teacher head0.410
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it