Effects of hypercapnia, hypocapnia, and hyperoxemia on brain morphometrics determined by use of T1-weighted magnetic resonance imaging in isoflurane-anesthetized dogs
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of various combinations of PaCO2 and PaO2 values on brain morphometrics. ANIMALS: 6 healthy adult dogs. PROCEDURES: A modified Latin square design for randomization was used. Dogs were anesthetized with propofol (6 to 8 mg/kg, IV), and anesthesia was maintained with isoflurane (1.7%) and atracurium (0.2 mg/kg, IV, q 30 min). Three targeted values of PaCO2 (20, 40, and 80 mm Hg) and 2 values of PaO2 (100 and 500 mm Hg) were achieved in each dog, yielding 6 combinations during a single magnetic resonance (MR) imaging session. When the endpoints were reached, dogs were given at least 5 minutes for physiologic variables to stabilize before T1-weighted MR images were obtained. Total brain volume (TBV) and lateral ventricular volume (LVV) were calculated from manually drawn contours of areas of interest by use of a software program, with each dog serving as its own control animal. Three blinded investigators subjectively evaluated the lateral ventricular size (LVS) and the cerebral sulci width (CSW). Brain morphometric values were compared among the target blood gas states. RESULTS: No significant differences in TBV were found among target states. The LVV was significantly greater during hypocapnia, compared with hypercapnia at the same PaO2 value. With regard to the subjective evaluations, there were no significant differences among evaluators or among combinations of PaO2 and PaCO2 values. CONCLUSIONS AND CLINICAL RELEVANCE: The changes observed in LVV during hypocapnia and hypercapnia may serve as a potential confounding factor when neuromorphometric evaluations are performed in anesthetized dogs.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".