Optimizing Vascular Access for Patients Receiving Intravenous Systemic Therapy for Early-Stage Breast Cancer—A Survey of Oncology Nurses and Physicians
Bibliographic record
Abstract
Background: Despite advances in systemic therapy choices for patients with early-stage breast cancer, optimal practices for intravenous (IV) access remain unknown. That lack of knowledge holds particularly true for the use of central venous access devices (cvads) such as peripherally inserted central catheters (piccs) and implanted vascular access devices (ports). Methods: Using a survey of Canadian oncologists and oncology nurses responsible for the care of breast cancer patients, we evaluated current access practices, perceptions of complications, and perceptions of risk, and we estimated complication rates and evaluated perceived risk factors for lymphedema. Results: Survey responses were received from 25 physicians and 57 oncology nurses. Administration of trastuzumab or an anthracycline was associated with a higher likelihood of a cvad being recommended. Other factors associated with recommendation of a cvad included prior difficult IV access and a recommendation from the chemotherapy nurse. Although the complication rates perceived to be associated with the use of piccs and ports remained high, respondents felt that cvads might improve patient quality of life. Risk factors perceived to be associated with the risk of lymphedema were axillary lymph node dissection, radiation to the axilla, and line-associated infection. Factors known to be unrelated to lymphedema risk (specifically, blood draws and blood pressure measurement) continue to be perceived as posing a higher risk. Conclusions: Despite widespread use of chemotherapy for patients with breast cancer, the type of venous access used for treatment varies significantly, as do perceptions about the risks of cvad use and the risk for lymphedema development. Further prospective studies are needed to identify best-practice strategies.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".