Indian Expert Consensus on the Selection of Vascular Access Devices in Oncology Patients
Bibliographic record
Abstract
To develop consensus recommendations for the selection and management of vascular access devices (VADs) in oncology patients in India by addressing unique challenges in the Indian healthcare ecosystem. An expert panel of 11 specialists in oncology and interventional radiology convened to review literature and develop consensus statements on 10 key questions related to the selection of VAD in cancer patients. The panel used a nominal group technique during a face-to-face meeting, achieving 100% consensus on all statements through structured discussions. The panel deliberated and agreed upon 10 consensus statements, addressing the indications for central venous access devices (CVADs), available CVAD options, clinical decision-making processes for identifying appropriate CVADs for different groups of patient, decision-makers for device selection and insertion, factors influencing port selection, the logistical requirements and CVAD maintenance protocols in cancer patients in India. Key recommendations emphasize the use of CVADs for administering vesicant drugs, considering both external catheters and implantable ports based on the duration and frequency of treatment. The recommendations also highlight the importance of involving multidisciplinary teams in device selection and implementing standardized training and protocols for insertion and maintenance. These consensus recommendations provide guidance on VAD selection and management tailored to the Indian oncology setting. The statements emphasize the importance of patient-centred decision-making, proper training for healthcare providers, and the need for increased awareness and education to enhance the appropriate adoption of implantable ports in India. Implementation of these recommendations may improve vascular access care and outcomes for cancer patients across diverse healthcare environments in India.
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 | 0.098 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".