The Blind Scientists and the Elephant of Swallowing: A Review of Instrumental Perspectives on Swallowing Physiology
Bibliographic record
Abstract
Abstract Swallowing is a complex biomechanical process. In this review, several different techniques for measuring swallowing physiology are described, and limitations of each instrumental perspective are discussed. The techniques discussed include videofluoroscopy, endoscopy, three‐dimensional dynamic computed tomography imaging, ultrasound, electromagnetic articulography, electromyography, lingual and pharyngeal manometry, electropalatography, airflow measurement, and swallowing acoustics/accelerometry. It is hoped that this review will inform scientists in the food oral‐processing field regarding methods that may be useful for capturing relevant features of swallowing behavior across different food textures and liquid consistencies. Likewise, it is hoped that the delineation of current gaps in knowledge will reveal topics of shared interest for swallowing and food oral scientists as a first step toward future collaboration. Practical Applications Swallowing is something that we take for granted, but is actually a complicated biomechanical activity involving many muscles. The process of swallowing can be studied or measured using a variety of different clinical and instrumental techniques. In this review article, the strengths and limitations of several different instrumental approaches to measuring swallowing behaviours are discussed. An extensive reference list is provided to original articles using these different techniques.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".