The Diagnosis of Choriocarcinoma in Molar Pregnancies: A Revised Approach in Clinical Testing
Bibliographic record
Abstract
BACKGROUND: Hydatidiform moles occur in approximately 1 in 1,500 pregnancies; however, early miscarriages or spontaneous abortions may not be correctly identified as molar pregnancies due to poor differentiation of chorionic villi. METHODS: The current clinical testing algorithm used for the detection of hydatidiform moles uses a combination of morphological analysis and p57 immunostaining followed by ploidy testing to establish a diagnosis of either a complete or partial molar pregnancy. We review here 198 referrals for fluorescence in situ hybridization (FISH) ploidy testing, where the initial diagnosis based on morphology is compared to the final diagnosis based on a combination of morphology, FISH and p57 immunohistochemical (IHC) staining. RESULTS: Approximately 40% of cases were determined to be genetically abnormal, but only 28.8% of cases were diagnosed as molar pregnancies. The underestimation of complete molar pregnancies and those with androgenetic inheritance was also found to be likely using conventional diagnostic methods, as atypical p57 staining was observed in approximately 10% of cases. CONCLUSIONS: Our findings suggest that a revised approach to testing products of conception is necessary, with cases screened according to their clinical history in order to distinguish molar pregnancy referrals from hydropic pregnancies.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".