Evaluation the Recurrence Risk of Gestational Trophoblastic Neoplasia (GTN) after Serum βhCG Normalization
Bibliographic record
Abstract
Background: Although many GTN patients can be treated with chemotherapy, a small proportion of them will relapse after complete recovery. To the best of our knowledge, there is not any information in respect of relapsed GTN cases in our region. In the current study we have aimed to evaluation of the recurrence risk of gestational trophoblastic neoplasia (GTN) after serum βhCG normalization Methods: This descriptive-analytical study was carried out on registered hospital data of patients with confirmed GTN diagnosis following molar pregnancy who admitted to the gynecology ward of Imam Khomeini Hospital between 2011 and 2017. Patients with diagnosis of postmolar GTN, based on at least five bhcg measurements was included. Patients information including initial level of serum BhcG, time to Bhcg resolution, types of molar pregnancy, treatment protocols, need to recuretage relapse, and finally, the period time between bhcg resolution to relapse were evaluated. Results: In the present study, 239 patients with GTN (including 180 complete and 59 partial moles) were evaluated. The mean age of the patients was 28.8 years, ranging from 16 to 47 years. The mean βhCG concentration was 170,000 IU/ml (ranged 760 to 850,000). The mean time of βhCG resolution was 8.19 months in the range of 4 to 12 months. Recurrence was observed in 9 patients (3.7%). The mean period time between βhCG resolution to relapse was 20.94 months. The mean initial level of βhCG was significantly lower in patients with recurrence (p <0.0001). The highest recurrence rate was seen in those receiving multiple-drug chemotherapy. There was also a significant relationship between disease stage and recurrence rate. Conclusion: The findings of this study indicate that although the recurrence of GTN is relatively low, given the poor prognosis of these patients, continuous evaluation of bHCG levels for at least two years is essential to prevent disease progression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".