Effects of hypercapnia, hypocapnia, and hyperoxemia on blood oxygenation level–dependent signal intensity determined by use of susceptibility-weighted magnetic resonance imaging in isoflurane-anesthetized dogs
Bibliographic record
Abstract
OBJECTIVE: To assess the effects of alterations in PaCO(2) and PaO(2) on blood oxygenation level-dependent (BOLD) signal intensity determined by use of susceptibility-weighted magnetic resonance imaging in brains of isoflurane-anesthetized dogs. ANIMALS: 6 healthy dogs. PROCEDURES: In each dog, anesthesia was induced with propofol (6 to 8 mg/kg, IV) and maintained with isoflurane (1.7%) and atracurium (0.2 mg/kg, IV, q 30 min). During 1 magnetic resonance imaging session in each dog, targeted values of PaCO(2) (20, 40, or 80 mm Hg) and PaO(2) (100 or 500 mm Hg) were combined to establish 6 experimental conditions, including a control condition (PaCO(2), 40 mm Hg; PaO(2), 100 mm Hg). Dogs were randomly assigned to different sequences of conditions. Each condition was established for a period of >or= 5 minutes before susceptibility-weighted imaging was performed. Signal intensity was measured in 6 regions of interest in the brain, and data were analyzed by use of an ANCOVA and post hoc Tukey-Kramer adjustments. RESULTS: Compared with control condition findings, BOLD signal intensity did not differ significantly in any region of interest. However, signal intensities in the thalamus and diencephalic gray matter decreased significantly during both hypocapnic conditions, compared with all other conditions except for the control condition. CONCLUSIONS AND CLINICAL RELEVANCE: In isoflurane-anesthetized dogs, certain regions of gray matter appeared to have greater cerebrovascular responses to changes in PaCO(2) and PaO(2) than did others. Both PaO(2) and PaCO(2) should be controlled during magnetic resonance imaging procedures that involve BOLD signaling and taken into account when interpreting findings.
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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 | 0.000 | 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".