Bibliographic record
Abstract
Ancillary techniques such as immunohistochemistry (IHC) enable the surgical pathologist to extract additional information from fixed, deparaffinized tissue specimens and to provide data critical to optimal clinical management of the patient. In this review of applications of IHC to the analysis of gynecologic malignancies, the usefulness of immunohistochemical analysis of neoplasms of the cervix, endometrium, and ovary is summarized. In the uterine cervix, dysplasia is associated with qualitative and quantitative alterations in the expression of the Ki-67 antigen expression, as well as an ability to detect human papillomavirus. Endometrial endometrioid adenocarcinomas display a highly characteristic immunophenotype, with coexpression of cytokeratin and vimentin and demonstration of foci of high molecular weight cytokeratin expression; in addition, IHC analysis of estrogen and progesterone receptor and p53 expression can provide important prognostic information about this tumor. Stromal tumors of the endometrium may display a partial smooth muscle immunophenotype, but novel markers such as CD10 provide new tools for the identification of these tumors. The immunophenotypes of the normal ovarian surface epithelium (OSE) and corresponding tumors display significant overlap with, but important distinctions from, mesothelium, and important new markers such as the Wilms tumor gene product can prove useful in the identification of carcinomas of the OSE. Important prognostic markers for carcinomas of the OSE include the HER-2/neu gene product and p53, alterations of which can both be assessed by IHC techniques. Finally, the recent availability of markers of ovarian stroma, including Melan-A and inhibin-alpha, has provided a means for the positive identification of ovarian stromal tumors, which can manifest protean histological appearances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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) | 0.004 | 0.002 |
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".