K167 as a Prognostic Indicator in Serous Types of Ovarian Malignancies
Bibliographic record
Abstract
Objective: To assess the frequency and distribution of Ki-67 immunoexpression across different histological subtypes and tumor grades of surface epithelial serous ovarian carcinomas. Methodology: This descriptive cross-sectional study was conducted at the Department of Pathology, SZABMU (PIMS), Islamabad, Pakistan, from January 15, 2022, to July 14, 2022. All patients fulfilling the inclusion criteria and visiting SZABMU, Islamabad, during the study period were included. All specimens were fixed in 10% formalin, followed by gross examination, sectioning, embedding in paraffin blocks, and preparation of hematoxylin and eosin (H&E)–stained slides. The slides were examined under a light microscope, and the diagnosis was recorded. Immunohistochemistry for Ki-67 was performed and evaluated accordingly. Results: The mean age of the study cohort was 52.4 years (SD ± 12.7). High-grade serous carcinoma constituted the majority of cases (62.9%), while low-grade serous carcinoma accounted for 37.1%. Evaluation of Ki-67 immunoexpression revealed low proliferative activity (1–30% staining) in 34.3% of cases, intermediate activity (31–50%) in 15.0%, and high proliferative activity (>50%) in 50.7%. Conclusion: High-grade serous carcinoma was identified as the predominant tumor grade, with Ki-67 immunoexpression >50% being the most consistent proliferative marker. These findings highlight the need for larger, multicenter studies in Pakistan to validate the observed trends and strengthen their clinical and prognostic significance.
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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 | 0.001 | 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".