Improved Outcomes in Stroke Thrombolysis with Pre-specified Imaging Criteria
Bibliographic record
Abstract
BACKGROUND: A 1995 National Institute of Neurological Disorders (NINDS) study found benefit for intravenous tissue plasminogen activator (tPA) in acute ischemic stroke (AIS). The symptomatic intracranial hemorrhage (SICH) rate in the NINDS study was 6.4%, which may be deterring some physicians from using this medication. METHODS: Starting December 1, 1998, patients with AIS in London, Ontario were treated according to NINDS criteria with one major exception; those with approximately greater than one-third involvement of the idealized middle cerebral artery (MCA) territory on neuroimaging were excluded from treatment. The method used to estimate involvement of one-third MCA territory involvement bears the acronym ICE and had a median kappa value of 0.80 among five physicians. Outcomes were compared to the NINDS study. RESULTS: Between December 1, 1998 and February 1, 2000, 30 patients were treated. Compared to the NINDS study, more London patients were treated after 90 minutes (p<0.00001) and tended to be older. No SICH was observed. Compared to the treated arm of the NINDS trial, fewer London patients were dead or severely disabled at three months (p=0.04). Compared to the placebo arm of the trial, more patients made a partial recovery at 24 hours (p=0.02), more had normal outcomes (p=0.03) and fewer were dead or severely disabled at three months (p=0.004). CONCLUSIONS: The results of the NINDS study were closely replicated and, in some instances, improved upon in this small series of Canadian patients, despite older are and later treatment. These findings suggest that imaging exclusion criteria may optimize the benefits of tPA.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".