Current trends in the surgical management of spontaneous lobar intracerebral hemorrhage in Canada: A nationwide survey
Bibliographic record
Abstract
ABSTRACT Background: The Early Minimally Invasive Removal of Intracerebral Hemorrhage (ENRICH) trial demonstrated that minimally invasive surgery to treat spontaneous lobar intracerebral hemorrhage (ICH) improved functional outcomes. We aimed to explore current management trends for spontaneous lobar ICH in Canada to assess practice patterns and determine whether further randomized controlled trials are needed to clarify the role of surgical intervention. Methods: Neurologists, neurosurgeons, physiatrists and trainees in these specialties were invited to complete a 16-question survey exploring three areas: (1) current management for spontaneous lobar ICH at their institution, (2) perceived influence of ENRICH on their practice and (3) perceived need for additional clinical trial data. Standard descriptive statistics were used to report categorical variables. The χ 2 test was used to compare responses across specialties and career stages. Results: The survey was sent to 433 physicians, and 101 (23.3%) responded. Sixty-eight percent of participants reported that prior to publication of the ENRICH trial, spontaneous lobar ICH was primarily managed conservatively, with surgery reserved for life-threatening situations. Forty-three percent of participants did not foresee a significant increase in surgical intervention at their institution. Of neurosurgical respondents, 33% remained hesitant to offer surgical intervention beyond lifesaving operations. Only 5% reported routinely using specifically designed technologies to evacuate ICH. Seventy percent reported that another randomized controlled trial comparing nonsurgical to surgical management for spontaneous lobar ICH is needed. Conclusions: There is significant practice variability in the management of spontaneous lobar ICH across Canadian institutions, stressing the need for additional clinical trial data to determine the role of surgical intervention.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".