Clinical studies and anti‐inflammatory mechanisms of treatments
Bibliographic record
Abstract
In this exciting era, we are coming closer and closer to bringing an anti-inflammatory therapy to the clinic for the purpose of seizure prevention, modification, and/or suppression. At present, it is unclear what this approach might entail, and what form it will take. Irrespective of the therapy that ultimately reaches the clinic, there will be some commonalities with regard to clinical trials. A number of animal models have now been used to identify inflammation as a major underlying mechanism of both chronic seizures and the epileptogenic process. These models have demonstrated that specific anti-inflammatory treatments can be effective at both suppressing chronic seizures and interfering with the process of epileptogenesis. Some of these have already been evaluated in early phase clinical trials. It can be expected that there will soon be more clinical trials of both "conventional, broad spectrum" anti-inflammatory agents and novel new approaches to utilizing specific anti-inflammatory therapies with drugs or other therapeutic interventions. A summary of some of those approaches appears below, as well as a discussion of the issues facing clinical trials in this new domain.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".