The Toronto Bedside Swallowing Screening Test (TOR-BSST)
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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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".