Feeding–swallowing difficulties in children later diagnosed with language impairment
Bibliographic record
Abstract
AIM: The aim of this retrospective study was to assess the relationship between feeding-swallowing difficulties (FSDs) and later language impairments in children. METHOD: Retrospective analyses were carried out using the clinical files of 82 children with language impairments from a large urban rehabilitation center. Two subgroups of these children were established: children with motor impairments, referred to as the language impairment with motor impairment ('LI+MI') subgroup (n=23, mean age 4y 6mo, SD 8.7mo), and children without motor impairments, referred to as the language impairment without motor impairment ('LI-MI') subgroup (n=59, mean age 5y, SD 8mo). The prevalence of food selectivity, difficulties in sucking, salivary control issues, and food transition difficulties was extracted. Data were compared with a general population estimate of FSDs. RESULTS: FSDs were documented in 62% of the clinical files; 87% of these files were from the LI+MI subgroup and 53% were from the LI-MI subgroup. Among each subgroup of children with language impairments, the prevalence of FSDs was significantly higher than the general population estimate of 20% (LI+MI:χ(2) =55.965, df=1, p<0.001; LI-MI: χ(2) =32.807, df=1, p<0.001). Furthermore, the prevalence of FSDs was significantly higher in children with language impairments and motor impairments than in those with language impairments but without motor impairments (χ(2) =6.936, df=1, p<0.01). Both food transition difficulties (χ(2) =14.99, df=1, p<0.001) and salivary control issues (χ(2) =5.02, df=1, p=0.02) were more frequent in the LI+MI subgroup than in the LI-MI subgroup. Combinations of two or more FSDs were also more frequent in the LI+MI subgroup than in the LI-MI subgroup (χ(2) =4.19, df=1, p=0.04). INTERPRETATIONS: These findings suggest that early FSDs may be used as a potential marker for language impairment. However, larger prospective studies are needed to confirm this.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".