Aphasia after acute ischemic stroke: epidemiology and impact on tertiary care resources
Bibliographic record
Abstract
Introduction. This study aimed to reveal the disease burden of aphasia after acute ischemic stroke (AIS) at the national level and investigate the impact of aphasia on tertiary care resources and patient outcomes. We aimed to investigate the length of stay (LOS) and discharge modified Rankin Scale (mRS) score in aphasic, acute ischemic stroke (AIS) patients in order to estimate aphasia-related disease burden at a national level. Material and method. The local database from the Cluj-Napoca Emergency County Hospital (CNECH), the second largest stroke center in Romania was used to export demographics, baseline clinical and laboratory data, inpatient length of stay (LOS), NIH Stroke Scale (NIHSS), and discharge modified Rankin Scale (mRS) score data for all AIS patients admitted during March 2019. Results and discussions. Of 92 patients included in the study, 30 (32.6 %) had aphasia on admission. In a marginally significant unadjusted hierarchical multiple regression model, individuals with aphasia had a LOS of 1.86 days longer than stroke survivors without aphasia. In an adjusted version of the model, the NIHSS score at baseline was a significant predictor for LOS. In addition, the presence of aphasia was associated with a 1.49 increase in the mean mRS score. Aphasia was a marginally significant predictor for increased LOS. Presence of aphasia was more likely to produce a poor functional outcome. Conclusions. Considering an estimated impact of approximately EUR 3 million on direct medical expenditure annually, future policymaking efforts should improve prevention of stroke and improved access to post-stroke aphasia care in Romania. Keywords: aphasia; acute ischemic stroke; length of stay; disability, outcome,
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".