Prevalence, Clinical Profile and Influence of Onset-to-Treatment Time for Subarachnoid Hemorrhage on Quality of Life in Patients: A Retrospective Study of a Decade
Bibliographic record
Abstract
Background: The objective of the current study was to outline the clinical profile of patients diagnosed with subarachnoid hemorrhage (SAH) due to intracranial aneurysm rupture, treated at a reference hospital in the interior of Sao Paulo, and to investigate the time elapsed between the ictus and the surgical approach and its relationship with motor deficits evaluated according to the modified Rankin Scale (MRS). Methods: This is a retrospective cohort, with data collection and analysis of patients with SAH between 2010 and 2020. Results: The results showed no correlation between MRS and: sex (P = 0.3459 nonparametric Mann-Whitney test), age range, and ethnicity (P = 0.5451 and P = 0.513, respectively, nonparametric Kruskal-Wallis test), affected arteries (P = 0.4801 nonparametric test of Kruskal-Wallis), and onset-to-treatment time for hemorrhage (rho = 0.02; P = 0.8204 in Spearman's nonparametric correlation test), adopting 5% significance. Conclusions: The prevalence of SAH in the hospital studied, and the profile and clinical aspects of the patients treated are similar to existing data in the literature. The onset-to-treatment time and site of the aneurysm did not influence the prognosis of the patients, who presented slightly better MRS levels than those found in the literature. J Neurol Res. 2024;14(2):68-73 doi: https://doi.org/10.14740/jnr769
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".