Effect of prior general anesthesia or sedation and antiseizure drugs on the diagnostic utility of wireless video electroencephalography in dogs
Bibliographic record
Abstract
BACKGROUND: Ambulatory wireless video electroencephalography (AEEG) is the method of choice to discriminate epileptic seizures from other nonepileptic episodes. However, the influence of prior general anesthesia (GA), sedation, or antiseizure drug (ASD) on the diagnostic ability of AEEG is unknown. HYPOTHESIS/OBJECTIVES: The use of sedation/GA or ASD treatment before AEEG recording may affect the diagnostic ability of AEEG and the time to first abnormality on AEEG. ANIMALS: A total of 108 client-owned dogs undergoing ambulatory AEEG for paroxysmal episodes. METHODS: Retrospective cohort study. Proportions of diagnostic AEEG and time to first abnormality were compared between dogs that received sedation/GA or neither for instrumentation as well as dogs receiving at least 1 ASD and untreated dogs. RESULTS: Ambulatory EEG was diagnostic in 60.2% of all dogs including 49% of the sedation/GA dogs and 68% of dogs that received neither (odds ratio [OR], 2.25; 95% confidence interval [CI], 1.02-5.00; P = .05). The AEEG was diagnostic in 51% of dogs receiving at least 1 ASD and 66% of untreated dogs (OR, 1.95; 95% CI, 0.9-4.3; P = .11). No difference was found in time to first abnormality between sedation/GA or neither or ASD-treated or untreated dogs (P = .1 and P = .3 respectively). Ninety-five percent of dogs had at least 1 abnormality within 277 minutes. CONCLUSION AND CLINICAL IMPORTANCE: Sedation/GA and concurrent ASD administration were not identified as confounding factors for decreasing AEEG diagnostic capability nor did they delay the time to first abnormality. A 4-hour minimal recording period is recommended.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".