Protocol for a systematic review of time to antibiotics (TTA) in patients with fever and neutropenia during chemotherapy for cancer (FN) and interventions aiming to reduce TTA
Bibliographic record
Abstract
BACKGROUND: Fever and neutropenia (FN) is a common complication of chemotherapy for cancer. Prompt empiric broad-spectrum antibiotic therapy in FN is typically considered standard of care, but the definition of prompt is not clear. We seek to systematically review the available data on the association between time to antibiotics (TTA) administration and clinical outcomes in patients with FN being treated with chemotherapy. There have been several efforts to reduce TTA in patients with FN, by implementing specific interventions, presuming there will be a beneficial effect on patient-important outcomes. This systematic review will also collect data on such interventions and their effect to reduce TTA and potentially change clinical outcomes. METHODS/DESIGN: The search will cover MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, EMBASE, CINAHL, CDSR, CENTRAL, and LILACS. A full-search strategy is provided. Lists of studies identified by references cited and forward citation searching of included articles will also be reviewed. Studies will be screened, and data extracted by one researcher and independently checked by a second. Confounding biases and quality of studies will be assessed with the risk of bias in non-randomised studies-of interventions (ROBINS-I) tool. Data will be presented in narrative and tabular forms; in addition, if appropriate data is available, random effects meta-analysis will be used to examine TTA. A detailed analysis plan, including an assessment of heterogeneity and publication bias, is provided. DISCUSSION: This study aims to evaluate the association between TTA and patient-important clinical outcomes. Additionally, it will identify, critically appraise, and synthesise information on performed interventions and its effect to reduce TTA as a way of gaining insight into the potential use of these approaches. This will provide better knowledge for an adjusted treatment approach of FN. SYSTEMATIC REVIEW REGISTRATION: PROSPERO [ CRD42018092948 ].
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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 | 0.082 | 0.109 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.018 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.089 | 0.012 |
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".