Percutaneous perfusion monitoring for the detection of hemodialysis induced cardiovascular injury
Bibliographic record
Abstract
INTRODUCTION: The safe delivery of hemodialysis (HD) faces dual challenges; the accurate detection of systemic circulatory stress producing cardiovascular (CV) injury, and the ability to enable effective preemptive intervention for such injury. We performed a pilot study to examine the capability of a new noninvasive, real-time monitoring system to detect the deleterious effects of HD on CV stability. METHODS: Monitoring System, Intelomed, Inc., Warrendale, PA, USA). Longitudinal strain (LS) values for 12 left ventricular segments were generated using speckle-tracking software (EchoPac, GE), to assess the presence of HD-induced regional wall motion abnormalities (RWMA), indicative of myocardial stunning. FINDINGS: A reduction in pulse strength (PS) of ≥40% detected by CVI was associated with the development of RWMA (P = 0.005). This reduction occurred in 6/8 patients, all of whom exhibited myocardial stunning. Two patients had no significant reduction in PS nor evidence of myocardial stunning. In subjects with cardiac stunning, the decrease in PS was evident early during HD, 11.49 ± 10 minutes into HD treatment, prior to the detection of RWMA, which were assessed at peak HD stress, mean 210 ± 16.43 minutes into HD treatment. DISCUSSION: Percutaneous perfusion monitoring, using pulse wave analysis, appears to be useful in identifying circulatory stress during HD and predicting the development of HD-induced myocardial stunning with a lead time long enough to consider timely intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".