International recommendations for a vascular access minimum dataset: a Delphi consensus-building study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it