Preliminary Experience Suggests the Addition of Choroid Plexus Cauterization to Functional Hemispherectomy May Reduce Posthemispherectomy Hydrocephalus
Bibliographic record
Abstract
BACKGROUND: Cerebral hemispherectomy can effectively treat unihemispheric epilepsy. However, posthemispherectomy hydrocephalus (PHH), a serious life-long complication, remains prevalent, requiring careful considerations in technique selection and postoperative management. In 2016, we began incorporating open choroid plexus cauterization (CPC) into our institution's hemispherectomy procedure in an attempt to prevent PHH. OBJECTIVE: To determine whether routine CPC prevented PHH without exacerbating hemispherectomy efficacy or safety. METHODS: A retrospective review of consecutive patients who underwent hemispherectomy for intractable epilepsy between 2011 and 2021 was performed. Multivariate logistic regression was used to identify factors independently associated with PHH requiring cerebrospinal fluid (CSF) shunting. RESULTS: Sixty-eight patients were included in this study, of whom 26 (38.2%) underwent CPC. Fewer patients required CSF shunting in the CPC group (7.7% vs 28.7%, P = .033) and no patients who underwent de novo hemispherectomy with CPC developed PHH. Both cohorts experienced seizure freedom (65.4% vs 59.5%, P = .634) and postoperative complications, including infection (3.8% vs 2.4%, P = .728), hemorrhage (0.0% vs 2.4%, P = .428), and revision hemispherectomy (19.2% vs 14.3%, P = .591) at similar rates. Patients without CPC had greater odds of developing PHH requiring CSF shunting (odds ratio = 8.36, P = .026). The number needed to treat with CPC to prevent an additional case of PHH was 4.8, suggesting high effectiveness. CONCLUSION: Preventing PHH is critical. Our early experience demonstrated that routinely incorporating CPC into hemispherectomy effectively prevents PHH without causing additional complications, especially in first-time hemispherectomies. A multicenter randomized controlled trial with long-term follow-up is required to corroborate the findings of our single-institutional case series and determine whether greater adoption of this technique is justified.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".