Prognostic Factors in Asian and White American Patients with Cervical Cancer, Considering Competing Risks
Bibliographic record
Abstract
Background: Cervical cancer is the 2nd most common malignant tumour in women worldwide. Previous research studies have given little attention to its prognostic factors in the rapidly growing Asian American population. In the present study, we explored prognostic factors in Asian and white American patients with cervical cancer, considering competing risks. Methods: The study included 58,780 patients with cervical cancer, of whom 54,827 were white and 3953 were Asian American, and for all of whom complete clinical information was available in the U.S. Surveillance, Epidemiology, and End Results database. Death from cervical cancer was considered to be the event of interest, and deaths from other causes were defined as competing risks. The cumulative incidence function and the Fine-Gray method were applied for univariate and multivariate analysis respectively. Results: We found that, for all patients (white and Asian American combined), the cumulative incidence function was associated with several factors, such as age at diagnosis, figo (Fédération internationale de Gynécologie et d'Obstétrique) stage, registry area, and lymph node metastasis. Similar results were found when considering white patients only. However, for Asian American patients, registry area was not associated with the cumulative incidence function, but the other factors (for example, figo stage) remained statistically significant. Similarly, in multivariate analyses, we found that age at diagnosis, figo stage, lymph node metastasis, tumour histology, treatment method, and race were all associated with prognosis. Conclusions: Survival status differs for white and Asian American patients with cervical cancer. Our results could guide the treatment of, and facilitate prognostic judgments about, white and Asian American patients with cervical cancer.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".