Bibliographic record
Abstract
We thank Bogani et al for their review of our article and their interesting comments. As we reported in our study, serous histology was associated with a lower response to therapy. The patients with grade 3 histology, however, were more likely to have a clinical benefit than patients with serous cancers (50% v 11%; P .018). The difference between grade 3 cancers was not statistically different from grade1 and 2 cancers (65%). In addition, we are not aware of existing data showing that patients with pretreated, recurrent endometrial cancer have a worse prognosis on the basis of histology. Our group has previously reported on single-agent activity with everolimus. The clinical benefit rate was 21%, and no patients demonstrated an objective response. Ma et al from the National Cancer Institute of Canada reported the single-agent activity of letrozole in a similar cohort to be 9.4%. Although cross-trial comparisons are not favored, the clinical benefit rate and response rate (40% and 32%, respectively) in this current study is favorable compared with the single-agent activity. In our trial, we also demonstrated a relatively favorable toxicity profile. We agree that there are alternatives for patients with recurrent endometrial cancer, particularly if they do not have systemic disease. Specifically, our exclusion criteria included patients who have isolated recurrences (vaginal, pelvic, or paraaortic) that are amenable to potentially curative treatment with radiation therapy or surgery. Unfortunately, patients with recurrent endometrial cancer often do not have curative disease, and we need to increase the successful treatment options. Our current randomized phase II trial of everolimus and letrozole or hormonal therapy (NCT02228681) will certainly help to determine if mammalian target of rapamycin inhibition should be used as an alternative to hormonal therapy in the management of this disease.
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 | 0.006 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.035 | 0.044 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".