Abandonment of High‐Dose Chemotherapy/Hematopoietic Cell Transplants for Breast Cancer Following Negative Trial Results
Bibliographic record
Abstract
OBJECTIVE: In 1999, three randomized controlled trials concluded that high-dose chemotherapy followed by autologous hematopoietic stem cell transplantation (HDC/HCT) is no better than conventional chemotherapy for women with breast cancer. This study documents the impact of the trials on use of HDC/HCT and describes how hospitals reacted to the trials. DATA SOURCE: We used patient-level data on 15,847 HDC/HCTs reported to the Center for International Blood and Marrow Transplant Research between 1994 and 2005. STUDY DESIGN: We report trends in total HDC/HCT procedure volume, compare the time to hospitals' exit from the HDC/HCT market between research and nonresearch hospitals, and document trends in hospital-specific volumes in the 2 years before exit. PRINCIPAL FINDINGS: HDC/HCT volume declined from 3,108 in 1998 to 1,363 the year after trial results were released. In 2002, only 76 procedures were performed. Teaching hospitals and the hospitals that participated in the trials were no slower to discontinue the procedure compared with nonteaching, nonparticipating hospitals. At the hospital level, volume declined steadily in the months before abandonment. CONCLUSION: The results suggest that comparative effectiveness research studies that report negative results can reduce spending, but specialists may be reluctant to relinquish cutting-edge technologies.
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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 | 0.122 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".