Systematic review of clinical prediction tools and prognostic factors in aneurysmal subarachnoid hemorrhage
Bibliographic record
Abstract
BACKGROUND: Clinical prediction tools assist in clinical outcome prediction. They quantify the relative contributions of certain variables and condense information that identifies important indicators or predictors to a targeted condition. This systematic review synthesizes and critically appraises the methodologic quality of studies that derive both clinical predictors and clinical predictor tools used to determine outcome prognosis in patients suffering from aneurysmal subarachnoid hemorrhage (SAH). METHODS: This systematic review included prospective and retrospective cohort studies, and randomized controlled trials (RCTs) investigating prognostic factors and clinical prediction tools associated with determining the neurologic outcome in adult patients with aneurysmal SAH. RESULTS: Twenty-two studies were included in this systemic review. Independent, confounding, and outcome variables were studied. Methodologic quality of individual studies was also analyzed. Included were 3 studies analyzing databases from RCTs, 8 prospective cohort studies, and 11 retrospective cohort studies. The most frequently retained significant clinical prognostic factors for long-term neurologic outcome prediction include age, neurological grade, blood clot thickness, and aneurysm size. CONCLUSIONS: Systematic reviews for clinical prognostic factors and clinical prediction tools in aneurysmal SAH face a number of methodological challenges. These include within and between study patient heterogeneity, regional variations in treatment protocols, patient referral biases, and differences in treatment, and prognosis viewpoints across different cultures.
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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 | 0.019 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".