Variations in Hyoid Kinematics Across Liquid Consistencies in Healthy Swallowing
Bibliographic record
Abstract
Purpose Judgments regarding hyoid movement are frequently included in evaluations of swallowing. However, the literature lacks reference values for measures of hyoid kinematics in healthy swallowing. This study explores hyoid movement across the continuum from thin to extremely thick liquids. Method Participants were 39 healthy adults under the age of 60 years (19 men) who underwent videofluoroscopy involving three sips each of 20% w/v thin barium and six sips each of slightly, mildly, moderately, and extremely thick barium. Half of the thickened stimuli were prepared using xanthan gum; and half, with a starch-based thickener. Sip volume was derived from pre- and post-sip cup weights. Hyoid position was tracked frame-by-frame relative to the anterior–inferior corner of C4. Measures of peak hyoid position (along the XY axis) were normalized to a C2–C4 scalar, and measures of time-to-peak position, speed, and time-to-peak speed were derived. As a first step, Spearman's correlations confirmed the influence of sip volume on these hyoid measures. Linear mixed-effects models then explored the effects of stimulus, sip volume, and task repetition on the dependent variables. Results The data set comprised 975 swallows with available hyoid tracking data. Sip volume was correlated with peak hyoid XY position ( r s = .15, p < .01), time-to-peak position ( r s = −.15, p < .05), and speed ( r s = .13, p < .01). No significant differences in hyoid kinematics were found across stimuli. Conclusion Measures of hyoid movement in healthy swallowing remain stable across the range from thin to extremely thick liquids with no systematic alterations in hyoid position or kinematics.
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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 | 0.001 | 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".