Scoping review protocol on the impact of antimicrobial resistance on cancer management and outcomes
Bibliographic record
Abstract
INTRODUCTION: Antimicrobial resistance (AMR) is a growing global public health concern and is becoming a significant challenge in the management of patients with cancer. Due to the immunosuppressive nature of cancer treatment, infection is a common complication and the necessary high usage of antibiotics increases the risk of AMR. Failure to adequately prevent and treat infection in patients with cancer as a result of AMR can increase the morbidity and mortality of the disease. The objective of this scoping review is to understand the relationship between AMR and cancer in order to develop effective antimicrobial stewardship in this patient population and minimise the detrimental effects of AMR on cancer outcomes. METHODS AND ANALYSIS: This scoping review will follow the Arksey and O'Malley methodology framework. An exploratory review of the literature on antibiotic resistance in cancer care will help to define the research questions (stage 1). A broad range of electronic databases (MEDLINE ALL, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and Embase) and search terms will be used to retrieve relevant articles published between 2000 and 2021 (stage 2). Studies will be systematically selected based on the eligibility criteria by two independent reviewers (stage 3). The titles and abstracts will be appraised to determine whether articles meet the eligibility criteria. This will be followed by screening of the full texts and only relevant publications will be retrieved. Data will then be extracted, collated and charted (stage 4); and the summary of aggregated results will be presented (stage 5). ETHICS AND DISSEMINATION: As this scoping review will collect and synthesise data from publicly available sources, no ethics review is required. When data collection and summarisation is completed, results will be disseminated through peer-reviewed publication and the key findings of the review will be presented at relevant conferences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".