Evolution of neurological recovery during the first year after subarachnoid haemorrhage in a French university centre
Bibliographic record
Abstract
INTRODUCTION: The evolution of neurological recovery during the first year after aneurysmal Subarachnoid Haemorrhage (SAH) is poorly described. PATIENTS: Patients with SAH in one university hospital from March the 1st 2010, to December 31st 2012, with a one-year follow-up. METHOD: Evaluation was performed via phone call at 3, 6 and 12 months. Primary endpoint was poor neurological recovery (modified Rankin Scale 3-4-5-6), one year after SAH. Secondary endpoints were the incidence of lack of self-perceived previous health status recovery and incidence of cognitive disorders, one year after SAH. Risk factors of poor neurological recovery were retrieved with multivariable logistic regression. RESULTS: Two hundred and eleven patients were included and 208 had a complete follow-up. One hundred and twenty (57.7%) patients were female, 112 (53.8%) had a WFNS grade I-II-III. Seventy (33.6%) patients displayed one-year poor neurological outcome and risk factors of poor outcome were age, baseline Glasgow Coma Score ≤ 8, external ventricular drainage, intra-cranial hypertension and angiographic vasospasm. We observed an improvement in good outcome at 3 months [112 (53.8%) patients], 6 months [127 (61.1%) patients] and one-year [138 (66.3%) patients]. Fifty-nine (35.3%) patients recovered previous health status, 96 (57.5%) had persistent behaviour disorders, and 71 (42.5%) suffered from memory losses at one year. DISCUSSION: Neurological recovery seems to improve over time. The same key complications should be targeted worldwide in SAH patients. CONCLUSION: Neurological complications in the following of SAH should be actively treated in order to improve outcome. The early neuro-ICU phase remains a key determinant of long-term recovery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".