Clinical practice guideline for the prevention of oral and oropharyngeal mucositis in pediatric cancer and hematopoietic stem cell transplant patients: 2021 update
Bibliographic record
Abstract
PURPOSE: To update the 2015 clinical practice guideline for the prevention of oral mucositis in pediatric cancer or hematopoietic stem cell transplant (HSCT) patients. METHODS: We performed seven systematic reviews of mucositis prevention. Three reviews included randomized controlled trials (RCTs) conducted in pediatric and adult patients evaluating cryotherapy, keratinocyte growth factor (KGF) or photobiomodulation therapy with a focus on efficacy. Three reviews included studies of any design conducted in pediatric patients evaluating these same interventions with a focus on adverse events and feasibility. One review included all RCTs of any intervention for mucositis prevention in pediatric patients. Primary outcome was severe oral mucositis. RESULTS: We included 107 unique studies of cryotherapy (22 RCTs and 4 pediatric studies); KGF (15 RCTs and 12 pediatric studies); photobiomodulation therapy (29 RCTs and 8 pediatric studies) and any intervention (31 pediatric RCTs). Effects on severe mucositis reduction from RCTs were cryotherapy risk ratio (RR) 0.49 and 95% confidence interval (CI) 0.31-0.76; palifermin RR 0.81 and 95% CI 0.69-0.95 and photobiomodulation therapy RR 0.40 and 95% CI 0.27-0.60. Cryotherapy was not feasible in young children while photobiomodulation therapy was feasible across age groups. Palifermin was associated with adverse effects. CONCLUSIONS: Cryotherapy should be used for older cooperative pediatric patients who will receive short infusions of melphalan or 5-fluorouracil. Intraoral photobiomodulation therapy (620-750 nm spectrum) should be used in pediatric patients undergoing autologous or allogeneic HSCT and for pediatric head and neck carcinoma patients undergoing radiotherapy. Palifermin should not be used routinely in pediatric cancer or HSCT patients.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".