Characterizing the Flow of Thickened Barium and Non-barium Liquid Recipes Using the IDDSI Flow Test
Bibliographic record
Abstract
The use of thickened liquids for dysphagia management has become wide-spread. Videofluoroscopy is commonly used to determine dysphagia severity and to evaluate the effectiveness of interventions, including texture modification, but this requires the use of radio-opaque contrast media. In order for the results of a videofluoroscopy to have validity with respect to confirming swallowing safety and efficiency on different liquid consistencies, it is important to understand the flow characteristics of the contrast media used and how the flow of these stimuli compares to the flow of liquids that are provided outside the assessment context. In this study, we explored the flow characteristics of 20% w/v barium and non-barium stimuli prepared using starch and gum thickeners to reach the slightly, mildly and moderately thick liquid categories defined by the International Dysphagia Diet Standardisation Initiative (IDDSI). Our goal was to identify recipes that would produce stimuli with stable flow properties over a 3 h time frame post mixing. Thickener concentration was titrated to achieve matching flow (i.e., IDDSI Flow Test results within a 1 ml range) across the four stimulus types (non-barium starch, non-barium gum, barium starch, barium gum) within each IDDSI level. The combination of barium and thickeners resulted in further thickening, particularly with starch-based thickening agents. A probe of the influence of refrigeration showed no difference in flow measures between chilled and room temperature stimuli over a 3-h time frame. Overall, recipes with stable flow over three hours were identified for all barium and non-barium liquids tested.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".