Frequency and predictors of hospitalization during chemotherapy: a systematic review.
Bibliographic record
Abstract
6583 Background: Hospitalization during chemotherapy is a significant event from both the patient and healthcare system perspectives but little is known about how often it occurs and in which settings. We conducted a systematic review to define the frequency of and factors associated with treatment-related hospitalization among cancer patients (pts) undergoing chemotherapy. Methods: A systematic search of Medline and EMBASE databases, from 1946 to September 2013, was undertaken to identify articles reporting rates of hospitalization in pts with cancer undergoing chemotherapy. Observational studies and clinical trials were eligible but results were analysed separately for each group. Summary statistics were used to describe the results and the Chi-square test was used to compare the groups. Results: Sixty articles met inclusion criteria: 44 observational studies comprising 189,342 pts and 16 randomized controlled trials comprising 13,086 pts. The majority of articles (80%) focused on chemotherapy given with palliative intent most commonly in breast, lung and colorectal cancers. The proportion of pts hospitalized at least once was significantly higher in observational studies at 32% (range 27-38%) compared with 21% (range 15-27%) in randomized trials (OR 2.13, 95%CI 2.03-2.23, p < 0.0001). A significant difference was seen in both the adjuvant and palliative settings between real life and trial pts with 42 vs 16% (OR 2.94, 95%CI 2.72-3.18, p < 0.0001) of adjuvant and 33 vs 23% (OR 3.24, 95%CI 3.03-3.48, p < 0.0001) of palliative pts being hospitalized, respectively. Factors associated with hospitalization in observational studies included higher comorbidity, type of chemotherapy and geographic location, while performance status and type of chemotherapy were significant predictors in clinical trials. Age was not a risk factor in either population. A number of methodological issues regarding reporting of hospitalization parameters were identified such as poor definitions of the at-risk period and attribution of hospitalization as treatment-related. Conclusions: Hospitalization during chemotherapy is common especially in unselected patient populations. However, few articles report this and often do so poorly.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".