Impact of an Etoposide Chemotherapy Shortage on Patients With Extensive-Stage Small-Cell Lung Cancer: Results of a Natural Experiment
Bibliographic record
Abstract
PURPOSE A shortage of essential intravenous (IV) etoposide lasted from 2018 until 2020 in Ontario, Canada, allowing for a natural experiment in which external factors (IV etoposide availability) dictated patients' treatment assignment. The purpose of this study was to evaluate the impact of this IV etoposide shortage (IVES) on patient care outcomes. METHODS Individuals with extensive-stage small-cell lung cancer (ES-SCLC) treated during a pre-IVES (November 2017-October 2018) and IVES (November 2018-October 2019) time intervals were retrospectively reviewed at the Verspeeten Family Cancer Centre. We investigated the association of the shortage on health care utilization and survival using a time-to-event analysis, Cox proportional hazards and logistic regression modeling. RESULTS A total of 119 patients with ES-SCLC were assessed, 49 in the pre-IVES interval and 70 in the IVES interval. The median age was 68 (IQR, 62-74) years, 48% (n = 57) were male, 33% (n = 39) had CNS metastases, and 69% (n = 82) received first-line systemic therapy. Alternate regimens used for IVES cohort included IV platinum-oral (PO) etoposide, IV platinum-IV irinotecan, and PO etoposide monotherapy. An adjusted multivariable model demonstrated a significant increase in hospitalization (odds ratio, 2.30 [95% CI, 1.01 to 5.24]; P = .047) and shorter progression-free survival (PFS; hazard ratio, 1.79 [95% CI, 1.19 to 2.68]; P = .005) during the IVES. CONCLUSION This study demonstrated increased hospitalization, and decreased PFS, among patients with ES-SCLC treated with alternate chemotherapy regimens during an IVES. The impact of cancer drug shortages can be harmful, and optimizing a more secure drug supply with mitigation strategies is warranted.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".