Bibliographic record
Abstract
Computed Tomography is an noninvasive imaging method for diagnosis of vertebral and spinal cord lesions in small animals. There are retrospectively evaluated CT studies at nine dogs presented at Madras Veterinary College Teaching Hospital, Chennai with various degrees of neurological deficit and suspected with vertebral or spinal cord lesions. Out of these eight dogs, Different breeds including five Labrador retriever, one Daushand, one German shepherd and one Non-descriptive breed. All the dogs reported with neurological deficit were male. The mean age was 5.43 Yrs. CT studies were performed under general anesthesia, dorsal recumbency, without contrast medium in 4 dogs and with contrast medium Iohexol 0.22ml/Kg B.wt into subarachnoid space at occipito–atlantial junction (CT- Myelography) in 3 dogs. There were found one vertebral fracture, one vertebral fracture with luxation, three disc herniations, one extramedullary intradural tumor, one myelomalacia and one dural calcification. In conclusion CT is a valuable diagnostic tool for detection and characterization of spinal lesions.
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 | 0.001 |
| 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 | 0.000 | 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".