Results of Diagnostic Investigations and Long-Term Outcome of 33 Dogs with Brain Infarction (2000–2004)
Bibliographic record
Abstract
Medical records of 33 dogs presented for acute onset, nonprogressive, intracranial dysfunction that had a magnetic resonance imaging diagnosis of brain infarction were reviewed. Postmortem confirmation of brain infarction was available in 10 dogs. All dogs were evaluated by CBC, serum biochemistry, thyroid and adrenal testing, urinalysis, thoracic and abdominal imaging, and cerebrospinal fluid analysis. Results of coagulation profile and arterial blood pressure were available in 32/33 and 28/33 dogs, respectively. On the basis of the imaging findings, infarcts were classified depending on their type (territorial or lacunar) and location within the brain (telencephalic, 10/33; thalamic/midbrain, 8/33; cerebellar, 15/33). No marked associations among location or type of infarct and patient age and sex, occurrence of systemic hypertension, and the presence or absence of a concurrent medical condition were identified. Small breed dogs (< or =15 kg) were significantly more likely to have territorial cerebellar infarcts, whereas large breed dogs (>15 kg) were significantly more likely to have lacunar thalamic or midbrain infarcts. A concurrent medical condition was detected in 18/33 dogs with brain infarcts, with chronic kidney disease (8/33) and hyperadrenocorticism (6/ 33) being most commonly encountered. Of 33 dogs, 10 were euthanized because of the severity and lack of improvement of their neurologic status or the severity of their concurrent medical condition. No association was identified between type or location of infarct and patient outcome. Dogs with concurrent medical conditions had significantly shorter survival times than those with no identifiable medical condition and were significantly more likely to suffer from recurrent neurologic signs because of subsequent infarcts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".