Abstract TP317: Enteral Tube Feeding in Acute Ischemic Stroke Patients: a Multivariate Predictive Analysis
Bibliographic record
Abstract
Introduction: Stroke is the most frequent cause of neurogenic oropharyngeal dysphagia. In the acute phase of stroke, the frequency of dysphagia is greater than 50%. The early clinical evaluation of swallowing disorders can help define approaches and avoid oral feeding, which may be detrimental to the patient. Hypothesis: The aim of this study was to identify predictive clinical factors associated with enteral tube feeding in acute ischemic stroke patients. Methods: The medical records of 326 acute ischemic stroke patients from our prospective stroke database were reviewed. Clinical factors as age, sex, comorbidities, blood pressure, glycemia, National Institutes of Health Stroke Scale (NIHSS) score and subscores, Glasgow Come Scale (GCS), previous Rankin, Alberta Stroke Program Early CT score (ASPECTS) and localization of acute stroke were analyzed. Logistic regression was used to develop a risk score by weighting predictors of enteral tube feeding placement based on strength of association. Results: Of the 326 patients, 84 used enteral feeding tubes (25.8%). The mean age (70.2 years - SD 13.1), mean GCS (12.7 - SD 2.1), mean NIHSS (12.6 - SD 5.6), and Aspect score (8.8 - SD 1.8) were significantly higher in the tube group. Logistic regression showed that only age (odds ratio [OR], 1.03; 95% confidence interval [CI], 1.00-1.6. P=0,025), NIHSS score (OR, 1.15; 95% CI, 1.05-1.25, P= 0,001) and NIHSS 10 (dysarthria) subscore (OR, 2.2; 95% CI, 1.2-4.05, P=0,011) were independent predictors of enteral tube feeding. A 3-item risk score was developed based on the regression model in order to identify those patients needing enteral feeding. A score ≥ 4 predicted tube feeding in 75% of cases. Conclusions: In conclusion, combining information about age, NIHSS, NIHSS 10 subscore, may be a useful predictor tool for clinical decision for enteral tube feeding in acute ischemic stroke patients.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".