Oropharyngeal dysphagia in older persons – from pathophysiology to adequate intervention: a review and summary of an international expert meeting
Bibliographic record
Abstract
Oropharyngeal dysphagia (OD) is a highly prevalent and growing condition in the older population. Although OD may cause very severe complications, it is often not detected, explored, and treated. Older patients are frequently unaware of their swallowing dysfunction which is one of the reasons why the consequences of OD, ie, aspiration, dehydration, and malnutrition, are regularly not attributed to dysphagia. Older patients are particularly vulnerable to dysphagia because multiple age-related changes increase the risk of dysphagia. Physicians in charge of older patients should be aware that malnutrition, dehydration, and pneumonia are frequently caused by (unrecognized) dysphagia. The diagnosis is particularly difficult in the case of silent aspiration. In addition to numerous screening tools, videofluoroscopy was the traditional gold standard of diagnosing OD. Recently, the fiberoptic endoscopic evaluation of swallowing is increasingly utilized because it has several advantages. Besides making a diagnosis, fiberoptic endoscopic evaluation of swallowing is applied to evaluate the effectiveness of therapeutic maneuvers and texture modification of food and liquids. In addition to swallowing training and nutritional interventions, newer rehabilitation approaches of stimulation techniques are showing promise and may significantly impact future treatment strategies.
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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 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".