Characteristics of traumatic intracerebral haemorrhage: An assessment of screening logs from the STITCH(Trauma) Trial
Bibliographic record
Abstract
INTRODUCTION: In undertaking international neurosurgical trials it is useful to understand international patient demographics and potential patient populations that study results will apply to. The STITCH(Trauma) trial included 59 centres from 20 countries, which were requested to screen all patients with traumatic intracerebral haemorrhage. This paper reviews these data. MATERIALS AND METHODS: Demographic, clinical and exclusion reason data were analysed. Comparisons were made between patients who were included in the trial and patients who were potentially eligible (but not included in the trial) and patients who were not potentially eligible. RESULTS: Screening evidence was returned for 1735 patients, 11% of these may potentially have been eligible, of whom 52% were not included because consent could not be gained. By country, median age per centre ranged from 26 years (Egypt) to 67 years (Germany), median time from injury to screening ranged from 5 h (Germany and Nepal) to 16 h (India), median intracerebral haemorrhage (ICH) volume ranged from 5 ml (Germany) to 30 ml (China), the proportion of male patients ranged from 56% (Egypt) to 91% (Canada) and the proportion of patients with both pupils reactive ranged from 68% (China) to 98% (Nepal). The most common exclusion reasons were ICH volume < 10 ml (49%) and presence of subdural haemorrhage/extradural haemorrhage or SDH/EDH requiring surgery (20%). CONCLUSION: Data presented here including international patient demographics and reasons for patient ineligibility will be useful for future traumatic ICH studies.
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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 | 0.032 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".