Access Flow Monitoring of Patients with Native Vessel Arteriovenous Fistulae and Previous Angioplasty
Bibliographic record
Abstract
Screening strategies based on measurement of access blood flow (Qa) allow detection and angioplasty of subclinical stenosis in native vessel arteriovenous (AV) fistulae. However, little is known about the efficacy of Qa measurements for detecting recurrent stenoses in fistulae and that of angioplasty for correcting them. A total of 303 patients were studied over 30 mo; 69 (23%) of these had stenoses, of whom 53 underwent angioplasty. Of those undergoing angioplasty, 30 patients had 46 episodes of recurrent positive studies and underwent repeat fistulography. In 31 of these episodes (19 patients), stenosis was again identified and treated successfully with angioplasty. Overall positive predictive values for stenosis were similar in first and subsequent episodes of stenosis (71% versus 67%), and angioplasty was associated with sustained increases in Qa for both first and subsequent episodes. Assisted patency in fistulae that required repeat angioplasty was 87% (median follow-up 10 mo after the second angioplasty). In conclusion, Qa is effective for detecting first and subsequent lesions in patients with AV fistulae, and angioplasty of first or subsequent lesions is associated with sustained increments in Qa. Continued screening after correction of first stenoses appears reasonable, because of both the frequency of recurrent stenosis and the success of repeat intervention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".