Vascular Access Support Team: A Multi-Disciplinary Response to Optimise Patients’ Care during COVID-19 Pandemic
Bibliographic record
Abstract
Objectives: To evaluate clinical outcomes of multidisciplinary vascular access support team (VAST) and the value of the service to critical care teams. Design: Prospectively collected data. Material and methods: All patients requiring vascular access at St Thomas’ Hospital, London over a 5-week period during the first wave of the pandemic in the UK. At the end of study period, online anonymised questionnaire administered to critical care team members, including nursing and medical professionals, to evaluate their experience of the service. Results: 122 patients aged 52.1 ± 13 years with high rate of pre-existing co-morbidities, underwent line insertion including 190 catheters (central venous n=182, arterial n=8). Median (range) number of 5 (0-17) lines were placed per day in patients of whom 90% tested positive for Severe Acute Respiratory Syndrome Coronavirus-type 2 pathogen (SARS-CoV-2). A single line was inserted in 146 out of 172 patients (76.8%) and n=36 patients (18.9%) ‘double puncture’ technique used. 45 line insertions (24%) had complications with minor [bleeding (n=19), line infection (n=10)] and 2 lines (1%) with major complication. The survey respondents, n=54 professionals, highlighted ease of referral and timely access placement (>90% responses); with agreement that VAST service saved them precious time and allow them to focus on other jobs. Conclusions: We describe the successful deployment of a multidisciplinary vascular access team with low complication rates and high rates of satisfaction. We recommend similar models can be considered by health services to optimise patient care and ICU management.
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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".