Telomere Length and Mortality Following a Diagnosis of Ovarian Cancer
Bibliographic record
Abstract
BACKGROUND: Telomeres are essential for the maintenance of chromosomal integrity. Telomere shortening leads to genomic instability, which is hypothesized to play a role in cancer development and prognosis. No studies to date have evaluated the prognostic significance of telomere length for ovarian cancer. METHODS: We examined whether relative telomere length in peripheral blood leukocytes was associated with survival following a diagnosis of ovarian cancer. We analyzed data from a large population-based study of incident ovarian cancer conducted in Ontario between 1995 and 2004. Telomere length was measured using the quantitative PCR-based relative telomere length assay and vital status was determined by computerized record linkage and by chart review (n = 1,042). Proportional hazard models were used to estimate ovarian cancer-specific survival HRs and 95% confidence intervals (CI) associated with quartiles of telomere length z score. RESULTS: We found no significant relationship between telomere length and ovarian cancer-specific mortality (P log-rank test = 0.55). Compared with women in the lowest quartile of telomere length z score, the HR for women in the highest three quartiles of telomere length z score combined was 0.88 (95% CI, 0.77-1.10). The corresponding estimates for serous and nonserous tumors were 0.68 (95% CI, 0.66-1.13) and 1.13 (95% CI, 0.71-1.79), respectively. CONCLUSIONS: Our data provide preliminary evidence that telomere length likely does not predict outcome after a diagnosis of ovarian cancer. IMPACT: This represents the first study to suggest no prognostic role of telomere length for ovarian cancer.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".