Real-World Effectiveness of Intravenous Stroke Thrombolysis is more than the Expectation of Practicing Neurologists
Bibliographic record
Abstract
BACKGROUND: The objective of the study was to compare the actual results of intravenous thrombolytic therapy (IVTT) in acute ischemic stroke with results anticipated by neurologists in practice. METHODS: Neurologists practicing in Thrissur metropolitan region, covering a population of 1.8 million, were telephonically surveyed about the number of yearly IVTT and their expert opinion/comment about effects of thrombolysis. This was compared with the results of IVTT from a single institution in the same region from 2012 to 2016. RESULTS: Eight neurologists in the region give approximately 140-150 IVTT per year. Nearly 20%-40% (median 32%) patients have good outcome, 5%-10% (median 9%) have intracerebral hematoma (ICH), and 25%-35% (median 30%) have death/bad outcome. Two neurologists from a tertiary care hospital in the region treated 122 cases of ischemic strokes with IVTT from 2012 to 2016. Age ranged from 8 to 88 years and 88 were males. Average delay in reaching hospital was 138.1 min and the door-to-needle time was 56.3 min. There were 26 cases of posterior-circulation strokes and 14 cases of cardioembolic strokes. At presentation, average National Institute of Health Stroke Scale (NIHSS) was 14.7; Modified Rankin Scale (mRS) 0.4; and CT Alberta Stroke Program Early Computerized Tomography Scores was 9.5. Good and sustained benefit (GSB) (>4 reduction in NIHSS at 24 h and 7 days) was there in 49% and no improvement (NI)/worsening in 36%. mRS 0-2 at discharge/30 days was documented in 57.3%. Symptomatic ICH was 10% (12/122) and mortality rate was 11.5% (14/122). GSB in posterior circulation strokes was 69.2% and NI/worsening in only 7.7%. mRS was 0-2 in 77% of posterior circulation strokes. CONCLUSION: Contrary to the popular belief of the practicing neurologists, IVTT has a high percentage of good outcome with a reasonable bleeding risk and low rates of absolute futility.
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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 | 0.015 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".