Interventions aiming to reduce time to antibiotics (TTA) in patients with fever and neutropenia during chemotherapy for cancer (FN), a systematic review
Bibliographic record
Abstract
PURPOSE: Multiple interventions have been developed aiming to reduce time to antibiotics (TTA) in patients with fever and neutropenia (FN) following chemotherapy for cancer. We evaluated their effect to reduce TTA and their impact on important clinical outcomes in a systematic review. METHODS: The search covered seven databases. Biases and quality of studies were assessed with the Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) tool. Interventions could be implemented in any setting and performed by any person included in the FN management. Absolute change of TTA was the primary outcome. Registration: PROSPERO (CRD42018092948). RESULTS: Six thousand two hundred ninety-six titles and abstracts were screened, 177 studies were retrieved and 30 studies were included. Risk of bias was moderate to serious in 28 studies and low in two studies. All but one study reported a reduction of TTA after the intervention. Various types of interventions were implemented; they most commonly aimed at professionals. Most of the studies made more than one single intervention. CONCLUSION: This review may help centers to identify their specific sources of delay and barriers to change and to define what intervention may be the best to apply. This review supports the assertion that TTA can be considered a measure of quality of care, emphasizes the importance of education and training, and describes the very different interventions which have effectively reduced TTA.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".