Retrospective Review Of The Use Of Palifermin To Prevent Mucositis In Hematopoietic Stem Cell Transplantation Patients Conditioned With High-Dose Chemotherapy And Total Body Irradiation
Bibliographic record
Abstract
Mucositis is caused by chemotherapy and radiation and is characterized by pain, erythema, and ulceration of the oral mucosa. Palifermin is a keratinocyte growth factor approved for the prevention of mucositis. The primary objective of this study was to compare the proportion, duration, and severity of mucositis in bone marrow transplant patients pre- and post-palifermin treatment. Data were collected retrospectively by a chart review on patients who received high-dose chemotherapy and total body irradiation from January 2004 to February 2009 at The Ottawa Hospital. Results were analyzed before and after the introduction of palifermin in August 2006. The severity of mucositis was assessed using the Bearman scale. Data were collected from 75 patient charts; 34 of which received palifermin. The proportion of patients experiencing mucositis was 97.6% for those who did not receive palifermin and 79.4% for those who received palifermin (p=0.02). Bearman scale grade≥2 mucositis was experienced by 92.7% of patients without palifermin compared to 47.1% with palifermin (p<0.001). Palifermin reduced the median duration of mucositis by 4.0 days (p=0.009). Palifermin reduced the proportion, severity, and duration of mucositis in this patient population.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".