Effect of prophylactic treatment with levetiracetam on the incidence of postattenuation seizures in dogs undergoing surgical management of single congenital extrahepatic portosystemic shunts
Bibliographic record
Abstract
OBJECTIVE: To report the incidence of postattenuation seizures (PAS) in dogs that underwent single congenital extrahepatic portosystemic shunt (cEHPSS) attenuation and to compare incidence of PAS in dogs that either did or did not receive prophylactic treatment with levetiracetam (LEV). STUDY DESIGN: Multi-institutional retrospective study. POPULATION: Nine hundred forty dogs. METHODS: Medical records were reviewed to identify dogs that underwent surgical attenuation of a single cEHPSS from January 2005 through July 2017 and developed PAS within 7 days postoperatively. Dogs were divided into 3 groups: no LEV (LEV-); LEV at ≥15 mg/kg every 8 hours for ≥24 hours preoperatively or a 60 mg/kg intravenous loading dose perioperatively, followed by ≥15 mg/kg every 8 hours postoperatively (LEV1); and LEV at <15 mg/kg every 8 hours, for <24 hours preoperatively, or continued at <15 mg/kg every 8 hours postoperatively (LEV2). RESULTS: Seventy-five (8.0%) dogs developed PAS. Incidence of PAS was 35 of 523 (6.7%), 21 of 188 (11.2%), and 19 of 228 (8.3%) in groups LEV-, LEV1, and LEV2, respectively. This difference was not statistically significant (P = .14). No differences between groups of dogs that seized with respect to investigated variables were identified. CONCLUSION: The overall incidence of PAS was low (8%). Prophylactic treatment with LEV according to the protocols that were investigated in our study was not associated with a reduced incidence of PAS. CLINICAL SIGNIFICANCE: Prophylactic treatment with LEV does not afford protection against development of PAS. Surgically treated dogs should continue to be monitored closely during the first 7 days postoperatively for seizures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".