Anterior versus Posterior: Evolving Evidence in Callosotomy for Drug-Resistant Epilepsy
Bibliographic record
Abstract
Dear Editor,We thank Prof. Martins et al. [1] for their thoughtful and articulate comments on our recent review and clinical decision-making framework on contemporary neurosurgical approaches for generalized drug-resistant epilepsy [2]. Their perspective in support of selective posterior corpus callosotomy (CC) is well received, and we agree that mounting evidence supports its potential role as a safe and effective option for controlling drop attacks.Historically, selective anterior CC was preferred based on the hypothesis that sectioning posterior callosal fibers might cause greater disruption of interhemispheric motor and sensory integration, leading to disconnection syndromes [3, 4]. As discussed in our review and reiterated by Prof Martins and colleagues, recent decades have seen the emergence of posterior callosotomy, targeting the splenium, isthmus, and posterior body of the corpus callosum in select patients with generalized DRE [5‒7]. Multiple large series have demonstrated significant reductions in drop attacks following selective posterior CC, with minimal cognitive morbidity [5, 7]. These benefits may reflect complete disruption of transcallosal fibers connecting sensorimotor regions compared to selective anterior CC [7]. We fully agree with our colleagues that comparative studies would be useful to delineate the relative efficacy and safety profiles of selective anterior versus posterior CC for the treatment of generalized DRE.We also suspect that a “one size fits all” – anterior versus posterior – approach may leave little room for nuance in surgical decision-making. Certain patients may benefit from one approach over another (or often from a complete callostomy). Advances in functional brain mapping may inform individualized risk profiles and guide personalized treatment decisions. To this end, tractography and functional MRI have shown early promise in mapping functionally significant connections within the corpus callosum on an individualized basis [8‒12]. Such technological advances may facilitate more tailored, patient-specific intervention that could minimize neurocognitive risk.In conclusion, we welcome the insightful additions of Prof. Martins and colleagues, which highlight the growing recognition of selective posterior callosotomy as an important addition to the surgical armamentarium for generalized drug-resistant epilepsy. We anticipate that ongoing comparative studies, combined with advanced brain mapping technologies, will prove effective in determining ideal surgical treatments for individual patients.K.M. has no conflicts of interest to disclose. G.M.I. receives consulting fees from Medtronic Inc. and also reports a relationship with Synergia Inc. and LivaNova Inc. that includes consulting and advisory fees and investigator-initiated funding. These organizations played no role in any part of the current work.The authors declare no funding was obtained or used for this study.K.M. and G.M.I. drafted and revised the manuscript.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".