Overview of cerebral cavernous malformations: comparison of treatment approaches
Bibliographic record
Abstract
OBJECTIVES: The comparison of treatment efficacy for cerebral cavernous malformations (CCMs) has not yet been well researched. DESIGN: PubMed, Cochrane Library, Science Direct, ISI Web of Science, Embase and additional sources were searched to identify cohort studies about the treatment of CCMs published between 1990 and 2020. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed; the Newcastle-Ottawa Scale was used to assess the risk of bias and to evaluate limitations based on selection/outcome biases. The cumulative incidences with 95% CIs were calculated using the random effects model. The models of Poisson distribution were applied to evaluate risk factors of poorer treatment outcome by calculating rate ratios within 100 person-years with 95% CIs. RESULTS: A total of 100 cohorts yielding 8994 patients treated for CCMs within 41 098 person-years of follow-up were analysed. The efficacy of ensuring the prevention of haemorrhage was 97% in surgical, 86% in radiosurgical and 77% in the conservative treatment. The lowest mortality (1%) was after radiosurgery, and the highest persistent morbidity (22%) was in natural history series. Deep-seated and brainstem CCMs were associated with higher bleeding rates. Lobar localisation was a protective factor in all analyses. Patients with history of previous haemorrhage were exposed to higher risk of rebleeding. Male gender was a protective factor associated with lower risk of post-treatment haemorrhage. CONCLUSIONS: Surgical resection of CCM is effective in ensuring the prevention of haemorrhage with acceptable morbidity and mortality, but conservative and radiosurgical management is a justified treatment alternative. Brainstem and deep-seated CCMs are predominantly associated with higher haemorrhage rates.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".