Safe handling of cytotoxics: guideline recommendations
Bibliographic record
Abstract
Objective: To investigate the clinicopathological, therapeutic, and survival data on pediatric major salivary gland cancers.Materials and Methods: National Cancer Database (NCDB) query from 2004 to 2018.Results: In total, 967 cases of individuals under the age of 21 were identified.Most cancers affected the parotid gland (86%).Mucoepidermoid carcinoma (41.3%) and acinic cell adenocarcinoma (33.6%) were the most common.Tumors occurred more often from age 11 to 21, and females were more affected.Histology varied by age, gender, and race.In the 0-5 age group, mucoepidermoid carcinoma and myoepithelial carcinoma/sarcoma/rhabdomyosarcoma were the most common pathologies.In patients over 5 years old, mucoepidermoid carcinoma was the most frequent tumor in boys, while acinic cell adenocarcinoma was more common in girls.African American patients had a higher incidence of mucoepidermoid carcinoma, while White patients in the 0-5 age group had a higher incidence of myoepithelial carcinoma/sarcoma/rhabdomyosarcoma tumors.Low-grade tumors were commonly diagnosed at stage I, but the 0-5 age group had a high frequency of stage IV tumors.The overall 5-year survival rate was 94.9%, with 90% for the 0-5 years age group and 96% for the 11-15 years age group.Negative margins were associated with higher 5-year survival rates in high-stage tumors (93%) compared to positive margins (80%).Submandibular malignancies had worse 5-year survival rates across all age groups.Conclusions: Major salivary gland malignancies in pediatric patients exhibit variations in histopathologic characteristics by age, gender, and race.Negative margins impact 5-year survival rates, especially in high-stage tumors.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".