Bibliographic record
Abstract
Abstract The kindling phenomenon is a form of sensitization where, with repetition, epileptiform discharges become progressively longer and behavioral seizures eventually appear and then become more severe. The classic or exogenous kindling technique involves the repeated application of a convulsant stimulus. This technique also lowers seizure thresholds, the minimum intensity of a stimulus required to evoke an electrographic seizure, a process known as epileptogenesis. Endogenous kindling typically occurs following a brain-damaging event which lowers seizure thresholds to the point where self-generated epileptiform discharges recur, lengthen, propagate, and drive progressively more severe behavioral seizures. While exogenous kindling results in alterations in neuronal molecular, cellular/synaptic, and network function that give rise to altered behavior, there is a paucity of evidence for loss of neurons. In contrast, brain-damaging events, with neuronal loss, typically give rise to endogenous kindling. Kindling is a pan-species phenomenon and all mammals that have been examined, including humans, manifest exogenous kindling when seizure-genic (forebrain) structures have been targeted. Since humans display both exogenous and endogenous kindling phenomena this serves as a sober warning to clinicians to prevent seizures. Kindling serves as a robust and reliable model for epileptogenesis, focal as well as secondarily generalized seizures, and certain epileptic disorders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.036 |
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".