An international expert consensus on improving the quality of care in patients with cancer by optimal central vascular access device selection.
Bibliographic record
Abstract
e23233 Background: In cancer patients (CPs), selection of an appropriate venous access is critical not only for delivering effective treatment, but also for enhancing patient comfort and quality of life to ensure treatment continuity. This consensus provides recommendations on the selection of the most appropriate central venous access device (CVAD) and its management in CPs. Methods: Eleven experts from three continents, including oncologists and healthcare professionals (HCPs) skilled in CVAD placement & maintenance, convened electronically and face-to-face at a 2-day meeting. A comprehensive review of clinical trials & guidelines on CVADs published between January 2013 and December 2023 from PubMed & Cochrane Library was conducted. Preliminarily drafted statements were discussed by the panel and voted on. Evidence and panelists' expertise was employed in an anonymous polling for consensus. The final recommendations were approved by all panelists. Results: A summary of the panel’s consensus (ten statements) is shown in Table 1. Conclusions: The present consensus provides a robust clinical framework for HCPs to select the most suitable CVAD to facilitate therapy for CPs. [Table: see text]
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 | 0.132 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".