The Effectiveness of Slightly Thick Liquids for Improving Swallowing in Bottle-Fed Children With Aerodigestive Concerns
Bibliographic record
Abstract
Purpose: Strategies for facilitating safe and functional bottle feeding in children with dysphagia include selecting nipples that reduce flow rate, pacing, altered positioning, and thickening liquid consistencies. We aimed to determine the impact of slightly thick liquids on swallowing through retrospective review of a convenience sample of clinical videofluoroscopic swallowing studies (VFSS) from 60 bottle-fed children (21 boys and 39 girls, mean age of 9.9 months) referred due to suspected aspiration. Method: Eligible VFSS exams were those in which the child swallowed both thin and slightly thick barium (40% w/v Varibar barium) using the same nipple. VFSS sequences (i.e., uninterrupted portions of the VFSS recording) were randomly assigned in duplicate for rating by trained raters; discrepancies were resolved by consensus. Parameters measured included number of swallows/sequence, sucks/swallow, swallow and sequence duration, number and timing of penetration or aspiration events, laryngeal vestibule closure integrity, and pharyngeal residue. Chi-square tests, linear mixed-model analyses of variance, and Wilcoxon signed-ranks tests identified consistency effects. Results: < .05), increased sucks/swallow, fewer swallows/sequence, and longer swallow and sequence durations. The number of children with ≥ 1 sequence showing pyriform sinus residue was significantly higher with slightly thick liquids. Conclusions: Slightly thick liquids can be effective in reducing penetration in bottle-fed children with dysphagia. However, slightly thick liquids may also lead to a safety-efficiency trade-off, with increased risk of pyriform sinus residue. Thickening for children with dysphagia should be considered only when other approaches are not effective. Overthickening should be avoided to limit negative impact on swallowing efficiency.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".