<i>In situ</i> assessment of the renal microcirculation in mechanically ventilated rats using sidestream dark‐field imaging
Bibliographic record
Abstract
For microcirculation research there is a need for baseline data and feasibility protocols describing microcirculation of various organs. The aim of our study was to examine the reliability and reproducibility of sidestream dark-field (SDF) imaging within the renal cortical microcirculation in rats. Renal microcirculation was observed using SDF probe placed on the exposed renal surface via the upper midline laparotomy. Video sequences recorded intermittently in short apneic pauses were analyzed off-line by using AVA 3.0 software (MicroVision Medical, Amsterdam, the Netherlands). Results are expressed as mean (SD) or median (25-75% percentiles). We obtained 60 clear sequences from all recorded analyzable videos from all the animals. The total small vessel and all vessel density (in mm.mm(-2) ) were (28.79 ± 0.40) and (28.95 ± 0.40), respectively. The perfused small and all vessel density were (28.79 ± 0.40) and (28.95 ± 0.40), respectively. The DeBacker Score was (19.14 ± 0.43), the proportion of perfused vessels was 100% (100-100%) and the microvascular flow index was 3.49 (3-3.75). We conclude SDF imaging provides a reliable method to examine the renal microvascular bed in vivo and thus can be used for the study of the renal cortical vascular network in various experimental diseases models and clinical settings.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".