Preliminary clinical evaluation of automated analysis of the sublingual microcirculation in the assessment of patients with septic shock: Comparison of automated versus semi-automated software
Bibliographic record
Abstract
INTRODUCTION: The outcome of patients in septic shock has been shown to be related to changes within the microcirculation. Modern imaging technologies are available to generate high resolution video recordings of the microcirculation in humans. However, evaluation of the microcirculation is not yet implemented in the routine clinical monitoring of critically ill patients. This is mainly due to large amount of time and user interaction required by the current video analysis software. The aim of this study was to validate a newly developed automated method (CCTools®) for microcirculatory analysis of sublingual capillary perfusion in septic patients in comparison to standard semi-automated software (AVA3®). METHODS: 204 videos from 47 patients were recorded using incident dark field (IDF) imaging. Total vessel density (TVD), proportion of perfused vessels (PPV), perfused vessel density (PVD), microvascular flow index (MFI) and heterogeneity index (HI) were measured using AVA3® and CCTools®. RESULTS: Significant differences between the numeric results obtained by the two different software packages were observed. The values for TVD, PVD and MFI were statistically related though. CONCLUSION: The automated software technique successes to show septic shock induced microcirculation alterations in near real time. However, we found wide degrees of agreement between AVA3® and CCTools® values due to several technical factors that should be considered in the future studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".