The cost of chemotherapy administration: A systematic review and meta-analysis.
Bibliographic record
Abstract
810 Background: Cancer treatment is a significant driver of healthcare costs worldwide, however, the economic impact of treating patients with anti-neoplastic agents is poorly elucidated. Hence, we conducted a systematic review and meta-analysis to estimate the direct costs associated with administering intravenous chemotherapy in an outpatient setting. Methods: We systematically searched four databases from 2010 to present and extracted hourly administration costs and the respective components of each estimate. Separate analyses were conducted of Canadian and United States (US) studies, respectively, to address a priori hypotheses regarding heterogeneity amongst administration cost estimates. The Drummond checklist was used to assess risk-of-bias. Data were summarized using medians with interquartile ranges and five outliers were identified; costs were presented in 2019 USD. Results: A total of 44 studies were analyzed, including sub-analyses of 19 US and seven Canadian studies. 26/44 studies were of moderate or high quality. When components of administration cost were evaluated, physician costs were reported most frequently (24 studies), followed by lab tests (13) and overhead costs (9). The median cost estimate when outliers were excluded was $142/hour (IQR = $103-166). Sensitivity analyses determined the median administration cost in the US was $149/hour (IQR = $118-158), and was $128/hour (IQR = $102-137) in Canada. Conclusions: There is currently a paucity of literature addressing the costs of chemotherapy administration, and existing studies utilize a patchwork of reporting methodologies which renders direct comparison challenging. Our results demonstrate that the cost of administering chemotherapy is approximately $125-150/hour, globally. This value is dependent upon the region of analysis, inclusiveness of cost subcomponents as well as the methodology used to estimate unit prices, as described here.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.025 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".