Incidence of and risk factors for seizures after myelography performed with iohexol in dogs: 503 cases (2002–2004)
Bibliographic record
Abstract
OBJECTIVE: To establish the incidence of and risk factors for seizures following myelography performed with iohexol in dogs. DESIGN: Retrospective case series. ANIMALS: 503 dogs. PROCEDURES: Medical records were searched for dogs that underwent myelography between April 2002 and December 2004. Data extracted included body weight, breed, age, sex, volume and dose of iohexol, site of injections, location of lesion, duration of anesthesia, surgical procedures immediately after myelography, use of acepromazine, and presence or absence of seizures. RESULTS: 15 (3%) dogs had postmyelographic seizures. Risk factors significantly associated with seizures were size of dogs (large dogs were 35.35 times as likely to have seizures as were small dogs), location of contrast medium injection (dogs in which iohexol was injected into the cerebellomedullary cistern were 7.4 times as likely to have seizures as were dogs in which iohexol was injected into the lumbar cistern), location of lesion (dogs with lesions at the level of the cervical portion of the vertebral column were 4.65 times as likely to develop seizures as were dogs with lesions in other regions), and total volume of iohexol. Mean ± SD total volume of iohexol was 11.73 ± 5.52 mL (median, 10.5 mL [range, 3.0 to 21.0 mL]) for dogs that had seizures and 4.57 ± 4.13 mL (median, 3.5 mL [range, 0.75 to 45.0 mL]) for those that did not. CONCLUSIONS AND CLINICAL RELEVANCE: Large-breed dogs with cervical lesions and large volumes of iohexol injected into the cerebellomedullary cistern had the highest risk of seizures. The use of contrast medium volumes > 8 mL in large dogs should be avoided, with preference given to injections into the lumbar cistern.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".