The Toronto Bedside Swallowing Screening Test (TOR-BSST)
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Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Dysphagia occurs in 55% of all acute stroke patients. Early identification of dysphagia from screening can lead to earlier treatments and thereby reduce complications. We designed and validated a new bedside dysphagia screening tool-the Toronto Bedside Swallowing Screening Test (TOR-BSST) for stroke survivors in acute and rehabilitative settings. METHODS: The TOR-BSST initially contained 5 items with proven high predictive ability for dysphagia. Trained screeners administer and score the TOR-BSST in less than 10 minutes. Trained nurses from 2 acute and 2 rehabilitation facilities administered the TOR-BSST to consecutively admitted stroke inpatients. A positive screen identified patients at risk for dysphagia. Blinded repeat screenings were conducted within 24 hours. Test-retest reliability was established with the first 50 administrations at an ICC=0.92 (CI, 0.85 to 0.96). Items were eliminated if they contributed <or=5% to the total score and were judged clinically impractical. 20% of all enrolled patients were randomly allocated to gold standard videofluoroscopic assessment of swallowing and findings rated independently by 4 blinded experts. Adequate validity was set at sensitivity >or=90% and negative predictive value >or=90%. RESULTS: 311 stroke inpatients were enrolled; 103 acute and 208 rehabilitation. Screening was positive in 59.2% acute and 38.5% rehabilitation patients. The pharyngeal sensation item did not meet inclusion criteria and was eliminated. The TOR-BSST demonstrated excellent validity with sensitivity at 91.3% (CI, 71.9 to 98.7), and negative predictive values at 93.3% in acute and 89.5% in rehabilitation settings. CONCLUSIONS: The TOR-BSST is a simple accurate tool to identify stroke patients with dysphagia regardless of severity and setting.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 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 it