Interventions to improve oral chemotherapy safety and quality: A systematic and grey literature review.
Bibliographic record
Abstract
97 Background: With the growing use of oral chemotherapy, there is an urgent need to develop safe and effective systems to administer and manage these agents. A comprehensive synthesis of literature on oral chemotherapy care delivery programs to which clinicians can look for best practices is lacking. Methods: We performed a systematic review of PubMed, EMBASE and CINAHL from 1/1995- 5/2016 and the grey literature to identify publications describing oral chemotherapy care delivery programs. Our population of interest was cancer patients of all ages prescribed cytotoxic or targeted anti-cancer oral agents. Interventions could address any part of the oral chemotherapy delivery process from prescribing through disposal but had to report outcomes (adherence and/or safety or toxicity) in relation to a control group. Results: From 7,984 abstracts in the peer-reviewed and 9 from the grey literature, 16 studies met inclusion criteria (7 of these randomized) with 3,612 patients represented. Interventions focused on prescribing (n = 1), preparation/dispensing (n = 2), education (n = 11), administration (n = 5), monitoring (n = 14), and storage/disposal (n = 1). Of the 10 articles with adherence as an outcome, four different measurement methods were used. Many articles lacked formal statistical testing. In the 6 studies with statistically significant improvement in outcomes, 3 utilized nursing phone calls to patients within the first few days of treatment initiation, which resulted in less toxicity (n = 2) or better adherence (n = 1). None of the four studies that evaluated eHealth strategies to increase patient to care team contact demonstrated a statistically significant improvement in outcomes. Conclusions: Limitations in study design impair our ability to draw definitive conclusions on best practices for oral chemotherapy care delivery. A framework for conducting research in this area that defines the processes of oral chemotherapy delivery and standardizes outcomes of success is needed to address this gap. Existing studies suggest that interventions focusing on education and remote phone-based monitoring of patients at therapy initiation may decrease toxicity, and possibly improve adherence.
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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 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".