Variability in Execution of the Chin-Down Maneuver by Healthy Adults
Bibliographic record
Abstract
OBJECTIVE: The chin-down maneuver is commonly used in dysphagia management to facilitate greater airway protection. However, the literature suggests that variation in maneuver execution may threaten the effectiveness of the intervention. Our goal was to study variation in chin-down maneuver execution given a uniform instruction. METHODS: Sagittal view digital video recordings were acquired from 408 healthy adults who performed sequences of reiterated water swallows in head-neutral and chin-down positions. Head angle measurements were extracted from the recordings, using markers on goggles worn by 176 participants. RESULTS: We observed considerable variation in head angle in the head-neutral swallowing task, with a trend to greater flexion in participants over the age of 65. Male participants showed greater variation in head angle than females. Head flexion during the chin-down swallowing tasks averaged 19°, in the range reported to yield clinical benefit in radiographic studies. CONCLUSION: We conclude that a clear, uniform instruction is adequate to facilitate execution of the chin-down maneuver to a degree that is likely to be of clinical benefit. The variation in head angle observed in this study warrants further research, particularly regarding the relationship between anatomical cervical spine curvature and head angle influence on swallowing.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".