Role of <scp>HER2</scp> in Prognosis of Salivary Duct Carcinoma: A Systematic Review and <scp>Meta‐Analysis</scp>
Bibliographic record
Abstract
Objectives Salivary duct carcinoma (SDC) is a rare, aggressive malignancy with a poor prognosis. These tumors frequently stain positive for HER2/ErbB2, but data on the prognostic significance of HER2 status in SDC are mixed. We sought to determine whether HER2 status affects survival outcomes in SDC. Methods PubMed, Embase, and Web of Science databases were searched from inception to October 2020. Eligibility was restricted to studies reporting HER2/ErbB2 overexpression in histologically confirmed de novo SDC or SDC ex pleomorphic adenoma, with corresponding overall (OS) and disease‐free (DFS) survival measures. Separate multivariable and univariable meta‐analyses were performed using random‐effects models. Statistical heterogeneity was estimated by Cochran's Q and I2 tests. Funnel plots were generated and Egger's test was used to assess for publication bias. The risk of bias was assessed with the Newcastle‐Ottawa Scale. Results Of 183 unique citations, 14 studies of 663 patients were included. Most included studies determined HER2 status according to ASCO/CAP guidelines. The univariable meta‐analysis did not reveal an effect between HER2 status and OS (HR 1.09, 95% CI 0.84–1.42). In the multivariable analysis, HER2 positivity was associated with a HR of 1.49 for OS (95% CI 0.96–2.30). Fewer studies reported data for DFS than OS, with no relationship between HER2 status and DFS found on multivariable or univariable meta‐analyses. Conclusion In patients with salivary duct carcinoma, HER2 positivity was not found to be associated with worse overall survival. This information may be useful when counseling patients and considering treatment options. Laryngoscope, 133:476–484, 2023
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".