Surveillance arterioveNous fistulAs using ultRasound (SONAR) trial in haemodialysis patients: a study protocol for a multicentre observational study
Bibliographic record
Abstract
INTRODUCTION: Arteriovenous fistulas (AVFs) are considered the best and safest modality for providing haemodialysis in patients with end-stage renal disease. Only 20% of UK centres achieve the recommended 80% target for achieving dialysis of the prevalent dialysis population via permanent access (as opposed to a central venous catheter). This is partly due to the relatively poor maturation rate of newly created fistulas, with as many as 50% of fistulas failing to mature.The Surveillance Of arterioveNous fistulAe using ultRasound study will examine whether a protocolised programme of Doppler ultrasound (US) surveillance can identify, early after creation, potentially correctable problems in those AVFs that subsequently fail to mature. METHODS AND ANALYSIS: This is a multicentre observational study that will assess newly created AVFs by Doppler US performed at 2, 4, 6 and 10 weeks after creation. The primary outcome measure will be primary fistula patency at week 10. Secondary outcome measures include: successful use of the fistula; clinical suitability for dialysis; creation of new fistula or radiological salvage; fistula thrombosis; secondary fistula patency rate and patient acceptability. ETHICS AND DISSEMINATION: The study has been approved by the Cambridgeshire and Hertfordshire Research Ethics Committee and by the Health Research Authority (REC 18/EE/0234). The results generated from this work will be published as open access, within 3 years of trial commencement. We will also present our findings at key national/international renal meetings, as well as support volunteers at renal patient groups to disseminate the trial outcome. TRIAL REGISTRATION NUMBER: ISRCTN36033877.
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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 | 0.047 | 0.039 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".