COMPARISON BETWEEN MAGNETIC RESONANCE IMAGING ESTIMATES OF EXTRACRANIAL CEREBROSPINAL FLUID VOLUME AND PHYSICAL MEASUREMENTS IN HEALTHY DOGS
Bibliographic record
Abstract
Dosages for myelography procedures in dogs are based on a hypothetical proportional relationship between bodyweight and cerebrospinal fluid (CSF) volume. Anecdotal radiographic evidence and recent studies have challenged the existence of such a defined relationship in dogs. The objectives of this prospective cross-sectional study were to describe CSF volumes using magnetic resonance imaging (MRI) in a group of clinically healthy dogs, measure the accuracy of MRI CSF volumes, and compare MRI CSF volumes with dog physical measurements. A sampling perfection with application optimized contrast using different flip-angle evolution MRI examination of the central nervous system was carried out on 12 healthy, male mongrel dogs, aged between 3 and 5 years with a bodyweight range of 7.5-35.0 kg. The images were processed with image analysis freeware (3D Slicer) in order to calculate the volume of extracranial CSF. Cylindrical phantoms of known volume were included in scans and used to calculate accuracy of MRI volume estimates. The accuracy of MRI volume estimates was 99.8%. Extracranial compartment CSF volumes ranged from 20.21 to 44.06 ml. Overall volume of the extracranial CSF increased linearly with bodyweight, but the proportional volume (ml/bodyweight kilograms) of the extracranial CSF was inversely proportional to bodyweight. Relative ratios of volumes in the cervical, thoracic, and lumbosacral regions were constant. Findings indicated that the current standard method of using body weight to calculate dosages of myelographic contrast agents in dogs may need to be revised.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".