Cerebral cavernous malformation remnants after surgery: a single-center series with long-term bleeding risk analysis
Bibliographic record
Abstract
The aim of this work is to investigate the long-term bleeding risk of cerebral cavernous malformation (CCM) remnants. A review of clinical, radiological, operative, and post-operative data of a cerebral cavernous malformation (CCMs) prospective database was performed. Fisher's exact test and Mann-Whitney U-test were used to assess differences between non-hemorrhagic and hemorrhagic CCM remnants for 14 variables. Recursive partitioning analysis was performed to assess the order of variables most associated with CCM remnant bleeding. Twenty-four patients out of 126 had a CCM post-surgical remnant. Of these, 7 had at least one post-operative hemorrhagic event. The mean follow-up was 80.7 months (range 12-144). CCM post-surgical remnant bleeding presented mostly with acute headache (50%) and focal neurological deficit (25%); in the remaining cases, the hemorrhage was asymptomatic. Retreatment was performed in two patients, with surgery and radiosurgery, respectively; no treatment was performed in the majority of cases. All patients ranked as non-II, according to Zabramski classification, did not show any post-surgical bleeding. The presence of a pre-operative perilesional hemosiderin ring was highly significant in predicting post-surgical bleeding (sensitivity = 0.94, specificity = 0.88) and incorrectly predicted bleeding in only two of the 24 patients. This study provides an evaluation of clinical and radiological factors influencing the bleeding risk of a CCM post-surgical remnant in a homogeneous population. Perilesional hemosiderin ring and Zabramski Type II appear to strongly condition the bleeding risk of a CCM post-surgical remnant.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".