Does Ki-67 Have a Role in the Diagnosis of Placental Molar Disease?
Bibliographic record
Abstract
The use of p57 immunohistochemistry (IHC) can distinguish complete mole (CM) from partial mole (PM) and nonmolar abortus (NMA). Molecular genotyping (MG) is the gold standard method for the definitive diagnosis of PM and NMA. However, MG is expensive and not always available. Some data suggest Ki-67 IHC may be helpful in distinguishing NMAs from PMs and could be a substitute for MG. In this study, we examined the utility of p57 and Ki-67 IHC stains in the diagnosis of placental molar disease. The study cohort consisted of 60 cases of products of conception (20 CMs, 20 PMs, and 20 NMAs). All CM cases showed absent (<10%) p57 IHC in chorionic villi. All PM and NMA cases had been subjected to MG and showed diandric triploid or biparental inheritance, respectively. Ki-67 and p57 IHC staining was done on formalin-fixed paraffin-embedded sections from all 60 cases. Both IHC stains were interpreted blinded to the diagnosis. On rereview, we recorded the percentage of cells with nuclear p57 staining in villous cytotrophoblast and stromal cells. Ki-67 proliferative index (%) was determined by manual count of at least 500 villous cytotrophoblastic cells in areas with highest Ki-67 reactivity. Any intensity of nuclear staining was considered positive. The utility of p57 IHC is mainly to exclude or confirm CM. Although there is a significantly higher Ki-67 expression in CMs in comparison to PMs and NMAs, this did not add diagnostic utility. PMs tend to have higher Ki-67 expression than NMAs; however, the difference is not statistically significant. Our data suggest that the use of p57 and Ki-67 IHC cannot reliably distinguish PM from NMAs.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".