A Prospective Cohort Study Comparing Long-Term Outcomes with and without Palifermin in Patients Receiving Hematopoietic Cell Transplantation for Hematologic Malignancies
Bibliographic record
Abstract
The incidence of debilitating oral mucositis (OM) can be as high as 99% after myeloablative conditioning regimens preparing patients with hematologic malignancies for hematopoietic cell transplantation (HCT). Palifermin (KGF) is a recombinant human keratinocyte growth factor that reduces the incidence and duration of severe OM. The long-term safety of KGF has not been well established, however. In this long-term prospective matched-cohort study, patients who received KGF (cases) and underwent autologous or allogeneic HCT for hematologic malignancies between 2006 and 2013 were matched 1:1 to patients who did not receive KGF (controls). The primary outcome was overall survival (OS). Other outcomes were disease relapse, new malignancies, pancreatitis, renal failure requiring dialysis, pulmonary complications, cataract surgery, and acute and chronic graft-versus-host disease (GVHD). The analysis population comprised 2191 matched pairs with a wide range of diseases and donor types that received diverse conditioning and GVHD preventive regimens, representing contemporary practice patterns. The median duration of follow-up was 8 years (range, 1 to 12.5 years). In multivariate analyses, the probabilities of OS (relative risk [RR], 1.01; 95% confidence interval [CI], 0.91 to 1.12), relapse (RR, 1.06; 95% CI, 0.94 to 1.18), new malignancies (RR, 0.89; 95% CI, 0.67 to 1.18), and cataract surgery (RR, 1.05; 95% CI, 0.74 to 1.50) were not statistically significantly different between cases and controls. In univariate analyses, no increased risks were observed for renal failure requiring dialysis, pancreatitis, acute GVHD, chronic GVHD, interstitial pneumonitis/acute respiratory distress syndrome/idiopathic pneumonia syndrome, or bronchiolitis obliterans/cryptogenic organizing pneumonia/bronchiolitis obliterans organizing pneumonia among cases compared with controls. This long-term prospective safety cohort study demonstrates that the KGF group had no increased risk of overall mortality, relapse, new malignancies, or any other key outcome. The broad inclusion criteria allow the results to be generalized to contemporary practice for patients with a wide range of diseases and receiving a wide range of HCT conditioning regimens and graft sources from diverse donor types.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".