Survival when treating adenoid cystic carcinoma of the external auditory canal: quantitative assessment of case reports.
Bibliographic record
Abstract
BACKGROUND: Adenoid cystic carcinoma (ACC) of the external auditory canal is a rare neoplastic condition. The purpose of this study was to conduct a quantitative review of case reports to assess the efficacy of treatment options and assess prognostic factors. METHODS: Cases were identified using PubMed. Kaplan-Meier curves were used to plot overall and disease-free survival. The log-rank test was used to compare survival curves in the univariate analysis for perineural invasion, margin status, and specific treatment modalities. A Cox proportional hazard model was used for multivariate analysis. RESULTS: Sixty-six cases were identified. The univariate analysis suggests an increased overall (p = .03) and disease-free (p = .03) survival for those treated with parotidectomies, whereas temporal bone resection decreased survival (p = .07). There was no overall or disease-free survival advantage using radiation (p = .8). Positive margins decreased both overall (p = .05) and disease-free survival (p = .02). Perineural invasion was not significant. The multivariate analysis confirmed the findings for parotidectomies (p = .02) and temporal bone resections (p = .01). CONCLUSIONS: Although the short-term survival for ACC is high, the risk of metastasis and poor long-term survival is high. In addition to local excision with negative margins, the surgeon should perform a parotidectomy.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".