Additional file 1 of Pre-hospital transdermal glyceryl trinitrate in patients with stroke mimics: data from the RIGHT-2 randomised-controlled ambulance trial
Bibliographic record
Abstract
Additional file 1. Table A. Baseline ambulance and hospital admission characteristics of Mimic versus non-Mimic patients enrolled in the RIGHT-2 trial. Data are number (%), median [IQR], or mean (standard deviation). Differences in means, medians and proportions are accompanied by 95% confidence intervals. Table B. Final diagnosis of mimics. Data are number (%). Table C. Cases whose qualifying event was described as an infection at any time-point (36 participants). Table D. Adherence and reasons for non-adherence in GTN versus sham groups. Data are number (%). Table E. Adherence and reasons for non-adherence in mimic versus non-mimic groups. Data are number (%). Table F. Protocol violations. Table G. Primary and main secondary outcomes at day 365 in all patients with a stroke mimic, except where stated. Data are number (%), median [IQR], or mean (standard deviation). Table H. In-hospital interventions. Table I. Neuroimaging on admission to hospital and day 2. Data are number (%), median [IQR], or mean (standard deviation). Table J. Causes of death among participants with a stroke mimic. Table K. Serious adverse events. Fig. A. Blood pressure curves. Fig. B. Forest plot of clinical and imaging information in participants with a stroke mimic. Fig. C. Boxplot of Day 90 mRS, by infection diagnosis (27 participants – one participant with infection is missing their mRS score).
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.728 | 0.050 |
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".