QT dispersion on ECG in acute ischemic stroke and its impact on early prognosis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of corrected QT dispersion (QTcd) on patients` prognosis with early stage non-lacunar ischemic stroke, regardless of location of the lesion. METHODS: In this non-randomized prospective study, stroke patients were evaluated in the intensive care unit of Cukurova University Hospital, School of Medicine, Adana, Turkey, from 2002-2003. Neurologic symptoms of all subjects were recorded according to Glasgow Coma Scale (GCS) and Canadian Neurological Scale. Subtypes of stroke were defined according to the Oxfordshire Community Stroke Project classification. Patients with GCS between 7 and 11 were included in the study. Electrocardiograms of the patients were collected in the first 6 hours. Corrected QT (QTc) were calculated by the Bazzett formula. Corrected QT dispersion was defined as maximum minus minimum QT interval. RESULTS: A total of 148 (74 male) consecutive acute stroke patients, aged between 36-90 years (mean 63.07 +/- 12.55), were divided into 2 groups. Group I consisted of surviving patients (n=109) and Group II consisted of expired patients (n=39). There were no statistically significant differences in the mean age, gender distribution, frequency of hypertension, diabetes mellitus, and coronary artery disease between the groups. Group II (7.4 +/- 3.7) had significantly higher QTcd (7.4 +/- 3.7) compared to Group I (p=0.002). CONCLUSION: This study shows the value of QTcd in predicting patients` prognoses with early stage non-lacunar ischemic stroke, regardless of location of the lesion.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".