Reply: The circular dilemma of seizure-induced brain injury
Bibliographic record
Abstract
Sir, We thank Dr Grillo for his comments. A growing body of evidence from in vitro studies, animal models, observational studies in critically ill neonates, children and adults supports the hypothesis that electrographic seizures independently contribute to brain injury, whether they are clinically evident or not (Holmes, 2014). However, the relative contribution of seizures to brain injury clearly depends on the underlying aetiology (Hahn and Jette, 2013). In our recent study, the association between seizure burden and outcome was stronger among children with acute seizures and systemic disease than among those with acute brain injury. Therefore, aggressive treatment to reduce seizure burden may be harder to justify among children with acute brain injury. However, even modest improvements in outcome, if sustained, may represent a substantial lifetime benefit to the child, their family and society. Treatment decisions should always be individualized, balancing the potential benefits of reducing seizure burden against the potentially serious complications of antiepileptic drug therapy, particularly when infusions of anaesthetic therapies are being considered. Clinical trials are urgently needed to address the unanswered question of whether more aggressive seizure treatment can improve outcomes among patients with electrographic seizures of varying aetiologies. Interventional studies on this topic pose ethical and logistical challenges. Current evidence for an association between seizure burden and outcome is a threat to the clinical equipoise required for any trial that proposes to randomize patients to more versus less aggressive seizure treatment. Obtaining informed consent from substitute decision makers with the timeliness required to initiate prompt anti-epileptic drug therapy is difficult. A promising alternative to a classical randomized controlled trial is a comparative effectiveness design, which harnesses the inherent variability of current treatment practice both within and among institutions. Access to continuous EEG monitoring remains a challenge in both resource-rich and resource-poor settings. Several clinical and EEG factors predictive of seizures among critically ill newborns, children, and adults have been identified (Claassen et al., 2004; McCoy et al., 2011; Abend et al., 2013; Glass et al., 2014), which facilitate allocation of scarce EEG monitoring resources to those most likely to benefit. This work was supported by a New Investigator Research Grant from The Hospital for Sick Children Foundation and the Canadian Institutes of Health Research (NI10-021), and a Health Research Grant from the Physicians' Services Incorporated Foundation (09- 40).
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.042 | 0.048 |
| 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".