Clinicopathological Predictors of Survival for Parotid Mucoepidermoid Carcinoma: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Various prognostic factors are associated with the survival of patients with parotid mucoepidermoid carcinoma (MEC). The aim of this systematic review is to summarize the clinical and pathologic prognostic factors on survival outcomes in patients with parotid MEC. DATA SOURCES: Articles published from database inception to July 2020 on OVID Medline, OVID Embase, Cochrane Central, and Scopus. REVIEW METHODS: Studies were included that reported clinical or pathologic prognostic factors on survival outcomes for adult patients with parotid MEC. Data extraction, risk of bias, and quality assessment were conducted by 2 independent reviewers. RESULTS: A total of 4290 titles were reviewed, 396 retrieved for full-text screening, and 18 included in the review. The average risk of bias was high, and quality assessment for the prognostic factors ranged from very low to moderate. Prognostic factors that were consistently associated with negative survival outcomes on multivariate analysis included histologic grade (hazard ratio [HR], 5.66), nodal status (HR, 2.86), distant metastasis (HR, 3.10-5.80), intraparotid metastasis (HR, 13.52), and age (HR, 1.02-6.86). Prognostic factors that inconsistently reported associations with survival outcomes were TNM stage, T classification, and N classification. CONCLUSION: Histologic grade, nodal status, distant metastasis, intraparotid metastasis, and age were associated with worse survival outcomes. These prognostic factors should be considered when determining the most appropriate treatment and follow-up plan for patients with parotid MEC.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".