Treatment of recurrent ovarian germ cell tumours: Is there a role for immune checkpoint inhibitors?
Bibliographic record
Abstract
• Chemo-resistant ovarian germ cell tumours (OGCTs) are difficult to treat. • No case of OGCT successfully treated with immunotherapy has been reported. • Few cases of response to immunotherapy have been described in testicular tumours, PD-L1 expression is not predictive. • Microsatellite-instability status should be tested on a sample of the recurrent tumour, as it may change during treatment. Ovarian germ cell tumours predominantly affect young women and have an excellent prognosis. While most contemporary papers concentrate on reducing treatment morbidity and preserving fertility, some women still succumb to refractory or recurrent OGCTs. Despite the significant impact of immune checkpoint inhibitors (ICIs) on many tumors, no case of a chemo-resistant ovarian germ cell tumour successfully treated with immunotherapy has been reported. In testicular cancer, only a few cases of partial response or stable disease to ICIs have been described. PD-L1 expression does not predict response, but microsatellite instability status may serve as a potential biomarker. MSI testing should be performed on a recurrent tumour sample as MSI status may evolve during treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".