Human Kallikrein 6 (hK6): A New Potential Serum Biomarker for Diagnosis and Prognosis of Ovarian Carcinoma
Bibliographic record
Abstract
PURPOSE: The discovery of new ovarian cancer biomarkers that are suitable for early disease diagnosis and prognosis may ultimately lead to improved patient management and outcomes. PATIENTS AND METHODS: We measured, by immunoassay, human kallikrein 6 (hK6) concentration in serum of 97 apparently healthy women, 141 women with benign abdominal diseases, and 146 women with histologically proven primary ovarian carcinoma. We then calculated the diagnostic sensitivity and specificity of this test and examined the association of serum hK6 concentration with various clinicopathologic variables and patient survival. RESULTS: Serum hK6 concentration between normal and benign disease patients was not different (mean, 2.9 and 3.1 micro g/L, respectively). However, hK6 in presurgical serum of ovarian cancer patients was highly elevated (mean, 6.8 micro g/L; P <.001). Serum hK6 decreased after surgery (to a mean of 3.9 micro g/L) in 68% of patients. The diagnostic sensitivity of serum hK6 at 90% and 95% specificity is 52% and 47%, respectively, in the whole patient population. For early stage disease (stage I or II), sensitivity is approximately 21% to 26%. When combined with CA-125, at 90% specificity, sensitivity increases to 72% (for all patients) and to 42% in stage I or II disease. Serum hK6 concentration correlates moderately with CA-125 and is higher in patients with late-stage, higher-grade disease and in patients with serous histotype. Preoperative serum hK6 concentration is a powerful predictor of disease-free and overall survival in both univariate and multivariate analyses. CONCLUSIONS: Serum hK6 concentration seems to be a new biomarker for ovarian carcinoma and may have value for disease diagnosis and prognosis.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".