Higher Incidence of In-Hospital Complications in Patients With Clipped Versus Coiled Ruptured Intracranial Aneurysms
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: After aneurysmal subarachnoid hemorrhage (SAH), patients with clipped aneurysms have a higher incidence of neurocognitive deficits and seizures compared with patients with coiled aneurysms. It remains unknown if patients with clipped aneurysms also have a higher incidence of other in-hospital complications. METHODS: We used data from the Registry of the Canadian Stroke Network on consecutive patients admitted to hospital with aneurysmal SAH. Patients who died within 2 days after admission were excluded. Baseline characteristics, incidence of various in-hospital complications within 30 days after admission, length of stay, poor functional outcome (modified Rankin Scale score at discharge of ≥3), and mortality were compared between patients with clipped versus coiled aneurysms. RESULTS: Of the 931 patients, 548 (59%) were clipped and 383 (41%) coiled. Baseline characteristics were similar. Compared with patients with coiled aneurysms, patients with clipped aneurysms had a higher incidence of in-hospital complications (37.2% versus 24.5% of patients; P<0.0001), poor functional outcome at discharge (69.4% versus 51.4%; P<0.0001), mortality (at discharge: 14.6% versus 9.1%; P=0.01), and a longer length of stay (17 [interquartile range, 11 to 29] versus 13 [interquartile range, 7 to 22] days; P<0.0001). Higher incidences were observed for urinary tract infection (P=0.02), pneumonia (P=0.01), cardiac/respiratory arrest (P=0.007), seizure (P=0.01), and decubitus ulcer (P=0.02). Urinary tract infection, pneumonia, cardiac/respiratory arrest, and seizure were independent predictors of poor functional outcome. CONCLUSIONS: Patients with clipped aneurysms have a higher incidence of in-hospital complications than patients with coiled aneurysms, which attributes to a higher risk of poor functional outcome and death and an increased length of stay.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".