Bibliographic record
Abstract
Traumatic brain injury is the leadingcause of death and disability amongpatients younger than 45 years of age, with mortality rates ranging from 30 % to 40%.1–3 Moreover, the impact of traumatic brain injury on quality of life among survivors is tremendous, with up to 30 % of patients acquir-ing major neurologic sequelae. Although few studies have compared mor-tality among centres in global trauma popula-tions,4,5 overall mortality and variation in mor-tality, specifically for patients with critical ill ness and traumatic brain injury, are less well de scribed. Because patients with severe trau-matic brain injury lack capacity for making medical decision s, relatives and medical teams must frequently estimate patients ’ preferences for treatment, including life support. Decisions to withdraw life-sustaining therapies are usually based on perceptions of unfavourable prognosis for meaningful neurologic recovery.6–8 How-ever, there are relatively few accurate and use-ful prediction tools to inform such estimates of prognosis. Therefore, prognostication is often Mortality associated with withdrawal of life-sustaining therapy for patients with severe traumatic brain injury: a Canadian multicentre cohort study
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.799 | 0.603 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".