International recommendations for a vascular access minimum dataset: a Delphi consensus-building study
Bibliographic record
Abstract
BACKGROUND: Data regarding vascular access device use and outcomes are limited. In part, this gap reflects the absence of guidance on what variables should be collected to assess patient outcomes. We sought to derive international consensus on a vascular access minimum dataset. METHODS: A modified Delphi study with three rounds (two electronic surveys and a face-to-face consensus panel) was conducted involving international vascular access specialists. In Rounds 1 and 2, electronic surveys were distributed to healthcare professionals specialising in vascular access. Survey respondents were asked to rate the importance of variables, feasibility of data collection and acceptability of items, definitions and response options. In Round 3, a purposive expert panel met to review Round 1 and 2 ratings and reach consensus (defined as ≥70% agreement) on the final items to be included in a minimum dataset for vascular access devices. RESULTS: A total of 64 of 225 interdisciplinary healthcare professionals from 11 countries responded to Round 1 and 2 surveys (response rate of 34% and 29%, respectively). From the original 52 items, 50 items across five domains emerged from the Delphi procedure.Items related to demographic and clinical characteristics (n=5; eg, age), device characteristics (n=5; eg, device type), insertion (n=16; eg, indication), management (n=9; eg, dressing and securement), and complication and removal (n=15, eg, occlusion) were identified as requirements for a minimum dataset to track and evaluate vascular access device use and outcomes. CONCLUSION: We developed and internally validated a minimum dataset for vascular access device research. This study generated new knowledge to enable healthcare systems to collect relevant, useful and meaningful vascular access data. Use of this standardised approach can help benchmark clinical practice and target improvements worldwide.
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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 | 0.504 | 0.411 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".