Rodent General Anesthesia Suitable for Measurement of Experimental Invasive Hemodynamics
Bibliographic record
Abstract
In cases of experimentally performed invasive rodent cardiovascular measurements, selected general anesthesia for a non-recovery procedure and its proper pain control plays a fundamental role in obtaining good data recordings. Rodent anesthesia is challenging for several reasons including high metabolic rate with elevated possibility of hypothermia and hypoglycemia during the procedure, large body surface area to adjust drug medication and anticipate drug clearance. In this review article, suitable analgesia, and anesthesia to collect rodent hemodynamics is discussed with examples of commonly used methods and anesthetic combinations to assess rodent hemodynamics. In case of injectable anesthesia, hemodynamic parameters should be measured when HR and mean arterial pressure (MAP) becomes stable. If re-injection is necessary, re-evaluation of HR and MAP is crucial for data integrity. Likewise, to safeguard data quality, longitudinal collection of HRs, HR variability, MAP and body temperature should be provided. For this reason, creation of a rodent hemodynamic anesthesia protocol might be necessary. In many cases, to refine surgical anesthetic protocol suitable for hemodynamic study, pilot experiments might be required to find the correct dose, and to probe for adequacy and duration of anesthesia, anticipating technical and procedural problems. Additionally, ensuring repeatability of the hemodynamic exam, selected experimental anesthetics should not be extensively metabolized. If metabolized, the effects on central and peripheral hemodynamics (HR, pre, afterload and contractility) should be well-known and documented.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".