Hospitalisations and emergency department visits in cancer patients receiving systemic therapy: Systematic review and meta-analysis
Bibliographic record
Abstract
Emergency department visits and hospitalisations (ED+H) during systemic therapy are undesirable for both patients and the health system. We undertook a systematic literature review and meta-analysis to evaluate the frequency of unplanned all-cause and treatment-related ED+H among adults receiving adjuvant or palliative-intent systemic therapy for all cancers. Randomised controlled trials (RCT) and observational studies (OS) reporting ED+H were identified from Medline and EMBASE from inception to June 2016. Quality was assessed using modified STROBE, CONSORT or PRISMA guidelines, depending on study type. A total of 112 OS (308,662 patients) and 26 RCTs (16,081 patients) met inclusion criteria. Most articles focused on palliative treatment (59%) delivered as first-line, in breast, lung and colorectal cancers. Only 20 articles reported ED frequency. Treatment-related and all-cause hospitalisations were more common in routine practice than in RCTs (29% vs. 16% and 42% vs. 28% respectively); frequency varied by treatment intent and tumour site. Methodological issues were common, particularly poor definition of the at-risk period. Hospitalisations are common, especially in unselected populations, but few articles report this and do so poorly. Routine, standardised reporting of ED+H during chemotherapy should be included in RCT reports and evaluated in routine care following adoption of new treatments.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.038 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".