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Record W1967629565 · doi:10.3148/67.4.2006.185

<i>Pediatric Feeding and Swallowing Problems</i>: An Interdisciplinary Team Approach

2006· article· en· W1967629565 on OpenAlexaffvenueabout
Shelley Williams, Krista Witherspoon, Peter A. Kavsak, Cyndi Patterson, Jacqueline McBlain

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2006
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsLakeridge Health
Fundersnot available
KeywordsSwallowingToddlerMedicineConfidence intervalPediatricsHealth careFamily medicinePsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

In 1995, Oshawa General Hospital (now Lakeridge Health Corporation, Oshawa site) developed an interdisciplinary feeding and swallowing clinic to serve children with feeding problems. After four years, a retrospective chart review of 104 subjects was completed to assess the performance of the clinic, which consists of a pediatrician, a speech-language pathologist (S-LP), an occupational therapist (OT), and a registered dietitian (RD). Goals were set at the initial and follow-up visits. These goals were individualized according to client needs and were related to improvements in growth and/or feeding abilities. During this period, 176 of 232, or 75.9% (70-81, 95% confidence interval), of the initial goals were attained by the first follow-up visit. Progress in the clinic, as measured by the number of goals achieved by the first follow-up visit, was further analyzed according to the patient age group/category (i.e., infant, toddler, and child) and by the health care professional (i.e., S-LP, OT, and RD) to ascertain and compare success rates in these groups and professionals. The overall success rates in the patient age groups (p=0.07) and among the different professionals (p=0.92) were not significantly different. In short, the interdisciplinary team approach proved successful in treating feeding problems in patients referred to the clinic.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.057
GPT teacher head0.382
Teacher spread0.324 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2006
Admission routes3
Has abstractyes

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