E.6 Return to work after aneurysmal subarachnoid hemorrhage: a systematic review of the literature and meta-analysis
Bibliographic record
Abstract
Background: Aneurysmal subarachnoid hemorrhage (aSAH) is a devastating disease process that represents a significant health shock for thousands of patients each year. Return to work outcomes and associated factors require evaluation to counsel patients and identify domains on which to focus clinical efforts. Methods: A systematic review of the literature following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines was performed using MEDLINE, EMBASE and Cochrane databases from inception to February 2024. Proportion of patients returning to work was collected from included studies. Odds ratios were pooled from studies evaluating the association between pre-rupture demographic variables, post-rupture clinical variables and return to work following aSAH. Results: Literature search yielded 3861 studies, of which 40 studies were included in the final analysis for a total of 6888 patients. On average, 55% (SD 17%) of all patients returned to work after an aSAH. Female sex (male sex OR 1.75), high grade aSAH on presentation (OR 0.30), and need for permanent CSF diversion (OR 0.50) are significantly associated with unemployment after aSAH. Conclusions: Female sex, high grade presentation, and permanent CSF diversion are associated with unemployment after aSAH. About half of all patients that experience aSAH return to work.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.042 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".