Dysphagia screening after intracerebral hemorrhage
Bibliographic record
Abstract
Background Dysphagia screening is recommended after acute stroke to identify patients at risk of aspiration and implement appropriate care. However, little is known about the frequency and outcomes of patients undergoing dysphagia screening after intracerebral hemorrhage (ICH). Methods We used the Ontario Stroke Registry from 1 April 2010 to 31 March 2013 to identify patients hospitalized with acute stroke and to compare dysphagia screening rates in those with ICH and ischemic stroke. In patients with ICH we assessed predictors of receiving dysphagia screening, predictors of failing screening, and outcomes after failing screening. Results Among 1091 eligible patients with ICH, 354 (32.4%) patients did not have documented dysphagia screening. Patients with mild ICH were less likely to receive screening (40.4% of patients were omitted, adjusted odds ratio (aOR) 0.40, 95% confidence interval (CI) 0.26-0.63). Older age, greater stroke severity, speech deficits, lower initial level of consciousness, and admission to intensive care unit were predictive of failing the screening test. Failing screening was associated with poor outcomes, including pneumonia (aOR 5.3, 95% CI 2.36-11.88), severe disability (aOR 4.78, 95% CI 3.08-7.41), and 1-year mortality (adjusted hazard ratio 2.1, 95% CI 1.38-3.17). When compared to patients with ischemic stroke, patients with ICH were less likely to receive dysphagia screening (aOR 0.64, 95% CI 0.54-0.76) and more likely to fail screening (aOR 1.98, 95% 1.62-2.42). Conclusion One-third of patients with ICH did not have documented dysphagia screening, increasing to 40% in patients with mild clinical severity. Failing screening was associated with poor outcomes. Patients with ICH were less like to receive screening and twice as likely to fail compared to patients with ischemic stroke, and thus efforts should be made to include ICH patients in dysphagia screening protocols whenever possible.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".