Evaluating the clinical utility and impact on healthcare utilization of serial troponin T monitoring in gynecologic cancer patients receiving immune checkpoint inhibitors – A single centre experience
Bibliographic record
Abstract
OBJECTIVES: The primary objective of this study was to report on the clinical utility of serial troponin T (cTnT) monitoring in patients with gynecological cancers receiving immune checkpoint inhibitors (ICIs). Secondary objectives were to describe the experience of a single centre within a public healthcare system and to discuss the associated increase in healthcare utilization resulting from this intense monitoring strategy. METHODS: We conducted a retrospective cohort study of all patients with endometrial, cervical, and vaginal cancers treated with ICIs at Sunnybrook Health Sciences Centre, Toronto, Canada, until June 2024. Serial cTnT was measured at baseline and prior to each cycle. Comprehensive clinical data was collected. Associations between cTnT elevation and outcomes were analyzed. RESULTS: Sixty-eight patients were included: 41 (60.3 %) with endometrial, 25 (36.8 %) with cervical, and 2 (2.9 %) with vaginal cancer. At baseline, 37.9 % had elevated cTnT. During therapy, 63.2 % experienced at least one troponin elevation above the upper normal limit. Troponin increases were associated with age, hypertension, and other immune-related adverse events, but not with overall survival. Two patients (2.9 %) developed confirmed ICI-induced myocarditis. In total, over 1400 cTnT assays were performed, leading to multiple downstream investigations and treatment delays without consistent clinical benefit. CONCLUSIONS: Serial cTnT monitoring frequently identified biomarker elevations but was not associated with outcomes in gynecologic cancer patients receiving ICIs. Despite a 63.2 % rate of elevated troponin, ICI-induced myocarditis occurred in 2.9 %. These findings suggest the need for evidence-based guidelines that balance early toxicity detection with safety, treatment continuity, and resource stewardship.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".