Prior History of Feeding–Swallowing Difficulties in Children With Language Impairment
Bibliographic record
Abstract
PURPOSE: This study updated and extended our previous investigation (Malas et al., 2015) of feeding-swallowing difficulties and concerns (FSCs) in children with language impairments (LI) by using more stringent inclusion criteria and targeting children earlier in the care delivery pathway. METHOD: Retrospective analyses were performed on the clinical files of 29 children (average age: 60 months, SD = 9.0) diagnosed as having LI using standardized testing, nonstandardized testing and final speech-language pathologist judgment. The files of children born prematurely or with a history of anatomical, structural, neurodevelopmental, cognitive, sensory, motor, or speech disorders were excluded. Literature-based indicators were used to determine the prevalence of difficulties in sucking, food transition, food selectivity, and salivary control. Values were compared with the general population estimate of Lindberg et al. (1992). RESULTS: A significantly higher percentage of histories of FSCs (48%) were found in the files of children with LI when compared with the population estimate (χ2 = 13.741, df = 1, p < .001). Difficulties in food transition (31%) and food selectivity (14%) were the most frequent. Data confirm and extend our previous findings and suggest that a previous history of FSCs may characterize children with LI early in their care delivery pathway.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".