Impact of Publicly Reported Outcomes on Patient Selection for Hematopoietic Cell Transplantation
Bibliographic record
Abstract
PURPOSE Public reporting of health care outcomes can have unintended effects such as inappropriate risk aversion in patient selection. METHODS The center-specific survival analysis annually assigns all hematopoietic cell transplantation (HCT) centers in the United States a +1, −1, or 0 score for observed outcomes that are above, below, or within a center-specific predicted range of outcome. For each index year (2012-2016), centers receiving a −1 score after 0 scores in the preceding 2 years were compared with contemporaneous centers with as-predicted outcomes (0 score). Changes in the patient population characteristics in the 3 years before versus the 3 years after the index years were compared between the newly below-expected centers (NBCs) and the controls. A multivariate model adjusted for baseline patient population characteristics and center volume. RESULTS No differences in patient selection behavior were identified when comparing the NBCs with the controls across eight key patient population characteristics. For the statistically modeled (predicted) 1-year overall survival (OS), reflecting a holistic measure of centers' patient population risk, we observed no statistically significant difference in change (−0.23% [95% CI, −1.4 to 0.9]; P = .70). The observed OS increased in both NBCs and controls by 0.9% and 4.5%, respectively, without statistically significant difference in change. CONCLUSION Centers receiving a −1 score were not observed to deviate significantly from patient selection trends in the HCT field. These findings suggest that public reporting of HCT outcomes in the United States does not result in unintended bias against HCT for high-risk patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".