Practice on an Acute Stroke Unit After Implementation of a Decision-Making Algorithm for Dietary Management of Dysphagia
Bibliographic record
Abstract
Dysphagia is a common disability seen in stroke survivors that has been associated with high morbidity and mortality. Research has indicated that implementing clinical guidelines and algorithms improves dysphagia management and patient outcomes. A decision-making algorithm designed to enhance the assessment and dietary treatment of swallowing difficulties in the acute stroke patient was implemented on a dedicated neuroscience unit in January 2002. Following implementation, the medical records of 30 acute stroke patients consecutively admitted to the unit between February and May 2002 were reviewed for stroke and dysphagia characteristics, dysphagia-related complications, discharge dispositions, interdisciplinary baseline assessments, and nursing evaluations throughout the hospitalization. Of those patients admitted with stroke, 56.7% were dysphagic. As compared with the nondysphagic patients, the dysphagic patients had three times' longer inpatient stay, an increased incidence of complications, higher morbidity, and increased need for inpatient rehabilitation services and institutionalized care following discharge. Twenty percent of patients did not receive aformal evaluation of swallowing function within the first 48 hours of admission. In 10% of the patients, diets were changed following the formal evaluation of swallowing to change an unsafe, prescribed diet. More than 70% of patients showed clinical improvement in swallowing function during their hospitalization. Nurses tended to document assessments of general neurological factors (e.g., level of consciousness) related to swallowing function more frequently than factors felt to be more specific to swallowing (e.g., choking) and nutrition (e.g., tolerates diet). The results support the important role of the neuroscience nurse in the early and ongoing assessment of swallowing function and in providing directions to further improve the quality of care delivered to stroke patients with various degrees of swallowing dysfunction.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".