Effects of stylet-in versus stylet-out collection of cerebrospinal fluid from the cisterna magna on contamination of samples, sample quality, and collection time
Bibliographic record
Abstract
OBJECTIVE: To evaluate safety of stylet-in and stylet-out techniques for collection of CSF from the cisterna magna and to assess whether there were differences between techniques with regard to contamination of samples, sample quality, and efficiency of collection. ANIMALS: 10 adult purpose-bred research Beagles. PROCEDURES: A prospective crossover study was conducted. Preanesthetic physical and neurologic examinations and hematologic analyses were performed. Dogs were anesthetized, and collection of CSF samples from the cisterna magna by use of a stylet-in or stylet-out technique was performed. Two weeks later, samples were collected with the other sample collection technique. Samples of CSF were processed within 1 hour after collection. RESULTS: Cellular debris was detected in higher numbers in stylet-in samples, although this did not affect sample quality. The stylet-out technique was performed more rapidly. No adverse effects were detected for either technique. CONCLUSIONS AND CLINICAL RELEVANCE: Both techniques could be safely performed in healthy anesthetized dogs. The stylet-out technique was performed more rapidly and yielded a sample with less cellular debris. Both techniques can be used in clinical practice to yield CSF samples with good diagnostic quality.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".