Reply: A comment on impaired peri-nidal cerebrovascular reserve in seizure patients with brain arteriovenous malformations
Bibliographic record
Abstract
Sir, We wish to thank the editor for offering us the opportunity to respond to this letter. We very much appreciate the detailed assessment of our work concerning the MRI assessment of vascular reserve in the vicinity of parenchymal brain arteriovenous malformations by Sturiale et al. Dr Sturiale’s primary issue with our study concerned the lack of comparison between our cerebrovascular reactivity findings and electrophysiological information that would have provided stronger support for our pathophysiological hypothesis relating seizures to reduced cerebrovascular reactivity. Our initial inclination was to make this correlation, but we quickly decided against it due to the well-known difficulties in localization of seizure foci in relation to the arteriovenous malformation nidus. Seizure foci are traditionally thought to be directly related to the arteriovenous malformations spatially, but there is good evidence that this is not always so. Support for a wider distribution of epileptogenic tissue including more distant sites secondary to ‘kindling’, was pointed out by Sturiale et al. citing the work of Yeh et al. (1990) in which EEG and intraoperative electrocorticography were performed preoperatively in 27 patients with arteriovenous malformations. Based on the EEG findings, excision of the epileptogenic tissue and arteriovenous malformation was required in 18 patients. In seven, remote seizure foci were identified, requiring further excision. Since the loss of vascular reserve observed in our study was localized to peri-nidal tissue, which, in the case of the grey matter, was within 8 mm of the arteriovenous malformation nidus, we would not have been able to make the more distant correlations with the EEG data. These facts outline the difficulty that we most certainly would have encountered in obtaining an accurate spatial correlation with electrophysiological data. This, in and of itself, does not necessarily weaken our hypothesis.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.039 | 0.038 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".