Identification of dysphagia using the Toronto Bedside Swallowing Screening Test (TOR-BSST<sup>©</sup>): Are 10 teaspoons of water necessary?
Bibliographic record
Abstract
Dysphagia screening often includes administration of water. This study assessed the accuracy in identifying dysphagia with each additional teaspoon of water. The original research of the TOR-BSST(©) permitted this assessment. Trained nurses from acute and rehabilitation facilities prospectively administered the TOR-BSST(©) to 311 eligible stroke inpatients. A sensitivity analysis was conducted for the water item using 10 teaspoons plus a sip as the standard. The proportion of positive screenings was 59.2% in acute and 38.5% in rehabilitation. Of all four items that form the TOR-BSST(©), the water swallow item contributed to the identification of dysphagia in 42.7% in acute and 29.0% in rehabilitation patients. Across all patients, dysphagia accuracy was that five teaspoons resulted in a sensitivity of 79% (95% confidence interval [CI] = 70-86), eight a sensitivity of 92% (95% CI = 85-96) and 10 a sensitivity of 96% (95% CI = 90-99). Although a primary contributor, the water swallow item alone does not identify all patients with dysphagia. For a water swallow to accurately identify dysphagia, it is critical to administer 10 teaspoons. The TOR-BSST(©) water swallow item contributes largely to the total TOR-BSST(©)'s screening score and in making the test highly accurate and reliable.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".