The Michigan Appropriateness Guide for Intravenous Catheters in Adult Patients With Cancer (MAGIC-ONC): Results From a Multispecialty Panel Using the RAND/UCLA Appropriateness Method
Bibliographic record
Abstract
Safe and reliable venous access is critical for high-quality cancer care. Patients with both solid and hematologic cancers require vascular access devices (VADs) for systemic chemotherapies and for supportive treatments, including blood products, antimicrobials, antiemetics, and fluids. However, VADs are associated with serious complications, including bloodstream infection and venous thromboembolism. Evidence-based guidance could maximize benefits and reduce risks in the selection and management of VADs in patients with cancer. The authors convened a 9-member international multidisciplinary panel and used the RAND/UCLA Appropriateness Method to develop recommendations for VAD selection, insertion, and management in patients with cancer. A literature review informed the development of clinical scenarios, which were rated by the panel for appropriateness based on cancer type, treatment indication, urgency, comorbidities, and anticipated duration of use. Of 1422 scenarios, 502 (35%) were rated as appropriate, 400 (28%) were rated as neutral/uncertain, and 520 (37%) were rated as inappropriate. Appropriateness of VAD selection varied by type of cancer, treatment urgency, and planned dwell time. For patients with acute hematologic cancers requiring urgent chemotherapy, placement of a double-lumen peripherally inserted central catheter (PICC) or a tunneled central venous catheter (CVC) was rated as appropriate, regardless of treatment intensity or infusate characteristics. For patients with malignant solid tumors, a single-lumen tunneled CVC or implanted port was rated as appropriate for delivering chemotherapy, regardless of treatment intensity, urgency, or duration. In patients with advanced chronic kidney disease, coordination of care with a nephrologist to ensure vein preservation in the context of cancer prognosis was recommended. By developing comprehensive, evidence-informed expert recommendations, the Michigan Appropriateness Guide for Intravenous Catheters in Adult Patients With Cancer (MAGIC-ONC) aims to improve clinical care, reduce complications, support quality improvement efforts, and advance the safety of vascular access for patients with cancer.
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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 | 0.021 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".