Mortality factors in high and ultra-high-risk gestational trophoblastic neoplasia at moi teaching & referral hospital: A decade-long observation in kenya
Bibliographic record
Abstract
Background: Prior to the advent of effective chemotherapy, Gestational Trophoblastic Neoplasia (GTN) resulted in fatality. The prognosis hinges on early detection and the provision of suitable and timely treatment. In low-middle-income countries (LMIC), delayed patient presentation and suboptimal treatment are common due to challenges such as insufficient staffing, limited resources, and inadequate infrastructure.Methods: Cross-sectional retrospective review of GTN database and medical charts of GTN patients managed at the Moi Teaching and Referral Hospital (MTRH) between January 2013 and December 2022. Logistic regression analysis was done to determine factors that were independently associated with mortality. Survival curves were generated using Kaplan Meier method.Results: Of the 98 cases analyzed, 71.4% were referrals from a different center. Majority of the women who died had heavy burden disease with 16/31 being ultra-high-risk while 26/31 had FIGO stages 3 and 4 disease. The lungs were the commonest site of metastasis in 39.8% of the cases while 11.2% of the patients had brain metastasis. An ECOG status of >1 (AOR 5.11 (95% CI: 1.543-19.218)), complications arising from either the disease or its treatment (AOR 5.112 95% CI: 1.087-24.045) and HCG levels ≥100000, (AOR 4.733 95% CI: 1.278-17.526) were identified as significantly correlated with mortality.Conclusion: Mortality associated with GTN is significantly high in LMICs and women with WHO score of ≥ 13 have an increased risk of death. Late presentations at advanced stages of disease, delay in treatment, and poor supportive care as well as healthcare constraints are thought to be predictors of mortality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".