Meeting the nutritional needs of patients with severe dysphagia following a stroke: an interdisciplinary approach.
Bibliographic record
Abstract
Dysphagia is a common problem with individuals who have experienced a stroke. The interdisciplinary stroke team noted delays in clinical decision-making, or in implementing plans for patients with severe dysphagia requiring an alternative method to oral feeding, such as enteral feeding via Dobhoff (naso-jejunum) or PEG (percutaneous endoscopic gastrostomy) tubes, occurred because protocols had not been established. This resulted in undernourishment, which in turn contributed to clinical problems, such as infections and confusion, which delayed rehabilitation and contributed to excess disability. The goal of the project was to improve quality of care and quality of life for stroke patients experiencing swallowing problems by creating a dysphagia management decision-making process. The project began with a retrospective chart review of 91 cases over a period of six months to describe the population characteristics, dysphagia frequency, stroke and dysphagia severity, and delays encountered with decision-making regarding dysphagia management. A literature search was conducted, and experts in the field were consulted to provide current knowledge prior to beginning the project. Using descriptive statistics, dysphagia was present in 44% of the stroke population and 69% had mild to moderate stroke severity deficit. Delays were found in the decision to insert a PEG (mean 10 days) and the time between decision and PEG insertion (mean 12 days). Critical periods were examined in order to speed up the process of decision-making and intervention. This resulted in the creation of a decision-making algorithm based on stroke and dysphagia severity that will be tested during winter 2002.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".