Assessment of diagnostic strategies based on risk stratification for aneurysmal subarachnoid hemorrhage: a retrospective chart review
Bibliographic record
Abstract
BACKGROUND AND IMPORTANCE: Current guidelines recommend noncontrast computed tomography (NCCT) followed by lumbar puncture for the diagnosis of subarachnoid hemorrhage (SAH). Alternative strategies, including clinical risk stratification and CT angiography (CTA), are emerging. OBJECTIVE: To evaluate alternative strategies to current guidelines through clinical risk stratification. DESIGN, SETTING AND PARTICIPANTS: Single-site, retrospective observational study of patients with SAH suspicion, from 2011 to 2016. We combined results of each investigation (NCCT, CTA and lumbar puncture) with a clinical risk assessment, including Ottawa score. EXPOSURE: Comparing the current strategy (NCCT ± lumbar puncture if negative CT) to alternative strategies (NCCT + CTA ± lumbar puncture if high clinical risk or negative CT and onset of headache ≥12 h o dds ratio ≥24 h). OUTCOME MEASURE AND ANALYSIS: Main outcome was diagnosis of SAH at hospital discharge. Secondary outcomes were death from all causes and need for invasive procedures at 28 days. We used sensitivity, specificity, positive predictive value and negative predictive value (NPV) to evaluate the diagnostic performance of three strategies. MAIN RESULTS: 310 patients were included. SAH was diagnosed in 8 cases (2.6%), none died and 7 (2.2%) had a surgical procedure. Performances of different strategies were not statistically different. NPVs were 99.7% [95% Confidence interval (CI), 98.2-100%] for strategy 1 and 100% (95% CI, 98.8-100%) for strategies 2 and 3. More than 4000 lumbar punctures are needed to diagnose one SAH when CTA is performed within 24 h of symptoms' onset and absence of high-risk criteria. CONCLUSION: Clinical risk stratification and CTA strategy are well-tolerated and effective for diagnosis of SAH, avoiding systematic use of lumbar puncture.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".