Assessment of microcirculatory function during hemodialysis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients with chronic kidney disease characteristically exhibit microcirculatory dysfunction, in combination with vascular damage. Hemodialysis superimposes additional circulatory stress to the microvasculature (repetitive ischemic insults/cumulative damage) resulting in high mortality. Intradialytic monitoring and hemodialysis delivery is currently limited to macrovascular/systemic assessment and detection of intradialytic systemic hypotension. Monitoring of the microcirculation has the potential to provide valuable information on hemodialysis-induced circulatory stress likely to result in end-organ ischemia (with/without systemic hypotension) generating an opportunity to intervene before tissue injury occurs. RECENT FINDINGS: Various noninvasive technologies have been used assessing the microcirculation in hemodialysis patients at rest. Some technologies have also been applied during hemodialysis studying the effects of treatment on the microcirculation. Despite the approach used, results are consistent. Hemodialysis patients have impaired microcirculations with treatment adding additional stress to inadequately regulated vascular beds. Utility/practicality/clinical relevance vary significantly between methodologies. SUMMARY: Intradialytic monitoring of the microcirculation can provide additional insights into a patient's individual response to treatment. However, this valuable perspective has not been adopted into clinical practice. A microcirculatory view could provide a window of opportunity to enable a precision medicine approach to treatment delivery improving current woefully poor subjective and objective clinical outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".