Benzodiazepines sensitivity testing. A pragmatic clinical approach to identify potentially useful GABAergic antiepileptic medications
Bibliographic record
Abstract
OBJECTIVE: To determine how benzodiazepine (BZD) sensitivity testing might be utilized to choose potentially useful antiepileptic drugs. METHODS: A retrospective audit of BZD sensitivity testing was carried out on 76 difficult pediatric epileptic cases that attended the Pediatric Neurology services at The Royal Hospital for Sick Children Edinburgh, Scotland from February 2005 to February 2006. The causes and types of epilepsy varied widely, as well as the encephalographic (EEG) findings. The EEG changes post-test are categorized according to the response to BZDs into "complete," "intermediate," "paradoxical" and "absent response." Similarly, the clinical outcomes after changing their antiepileptic medications have different ranges of clinical improvement from "definitive," "partial" and "no improvement." RESULTS: The largest percentages of definitive improvement are seen in those with complete response. The percentage with clinical improvement tends to decrease a) with increasing numbers and amplitudes of spikes that are resistant to the action of BZD, and b) when there is a paucity of, and different distribution of fast rhythms, indicating non-viability of cortical tissues. High spike density regions in the EEG pre-test that correlate with a specific pathology, and are found post-test to be devoid of fast rhythms, may indicate focally damaged gamma-aminobutyric acid receptor areas. CONCLUSION: The BZD sensitivity testing may influence the choice of anticonvulsants in the management of epilepsy.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".