ATM, THMS, and RRM1 protein expression in nasopharyngeal carcinomas treated with curative intent
Bibliographic record
Abstract
BACKGROUND: In advanced nasopharyngeal carcinoma (NPC), biomarkers may help predict survival. METHODS: Tumoral expression of ataxia-telangiectasia mutated (ATM), thymidylate synthetase (THMS), and ribonucleotide reductase subunit M1 (RRM1), was correlated with survival in patients with nonmetastatic NPC using quantitative fluorescence immunohistochemistry with automated quantitative digital image analysis. RESULTS: Of the 146 patients included, 58 patients (40%) received concurrent chemoradiation therapy; the remainder was treated with radiation. Overall survival (OS) at 5 years was 71% (95% confidence interval [CI], 62% to 78%); disease-free survival (DFS) was 48% (95% CI, 39% to 57%). OS worsened for increasing values of ATM (hazard ratio [HR], 2.83; 95% CI, 1.01-7.94; p = .049) for values greater than the 75th percentile compared to less than the 25th percentile, but improved for tumors with higher THMS levels (HR, 0.44; 95% CI, 0.20-0.94; p = .033) for values greater than the 25th percentile compared to less than or equal to the 25th percentile. RRM1 was not associated with OS (p = .748). No biomarkers were associated with DFS. CONCLUSION: In our cohort, relative overexpression of ATM and low THMS levels were associated with worse OS. © 2015 Wiley Periodicals, Inc. Head Neck 38: E384-E391, 2016.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".