STANDARDIZATION OF AN ACUTE CARE STROKE PATHWAY FOR SPEECH LANGUAGE PATHOLOGY
Bibliographic record
Abstract
STANDARDIZATION OF AN ACUTE CARE STROKE PATHWAY FOR SPEECH LANGUAGE PATHOLOGYP. Letsos1, S. Somers2.1University Hospital- London Health Sciences Centre, Speech Language Pathology, London, Canada.2University Hospital- London Health Sciences Centre, Speech-Language Pathology, London, Canada.Abstract TextBackgroundTo align with Canadian Stroke Best Practice Recommendations a standardized clinical pathway for Speech and Language Pathology (S-LP) was created and implemented for those requiring acute stroke services in an urban regional stroke centre (consisting of 26 stroke beds) in London, Ontario, Canada. Acute stroke services in Southwestern Ontario were re-aligned to optimize access to timely interventions, resulting in an increase in the number of patients being admitted to our regional centre. This caused the S-LP service delivery model shift from one clinician to several, providing a need for a coordinated, seamless pathway for assessment and management of communication and swallowing. MethodsAn audit of current services revealed that clinicians were utilizing various, non- standardized swallowing and communication assessment techniques that were not comprehensive or timely. Adverse outcomes from inconsistent S-LP practices included failure to identify the extent of oro-pharyngeal dysphagia and communication deficits impacting discharge planning, patient safety and quality of life. To effectively translate evidence into routine practice, a S-LP clinical pathway was implemented.Results Preliminary analysis revealed a near 50% increase in the administration of more comprehensive and timely communication assessments for those acute stroke patients admitted to our regional stroke centre. Further review of the data suggested an upward trend towards the provision of more frequent and specific oral care recommendations for those patients with dysphagia.Conclusion The implementation of a S-LP acute stroke pathway at an urban regional stroke centre has resulted in the standardization of care delivery irrespective of patients' day of hospital admission or service provider.
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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 | 0.064 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".