Second primary malignancies in females with primary fallopian tube cancer
Bibliographic record
Abstract
Primary fallopian tube cancer (PFTC) is a rare disease, and its aetiological factors are poorly understood. Studies on PFTC in the setting of 2nd primary malignant neoplasms can provide clues on aetiology and also define the possible side effects of different treatment modalities for PFTC. A cohort of 2,084 cases with first PFTC was extracted from the data from 13 cancer registries from Europe, Canada, Australia and Singapore and followed for second primary cancers within the period 1943-2000. Standardized incidence ratios (SIRs) were calculated and Poisson regression analyses were done to find out the RRs related to age at, period of and time since the PFTC diagnosis. There were 118 cancer cases observed after first PFTC (SIR 1.4, 95%CI 1.1-1.6). Elevated SIRs were seen for colorectal cancer (1.7, 95%CI 1.0-2.6), for breast cancer (1.5, 95%CI 1.1-2.2), for bladder cancer (2.8, 95%CI 1.0-6.0), for lung cancer (1.8, 95% CI 0.9-3.2) and for nonlymphoid leukaemia (3.7, 95%CI 1.0-9.4). Significant risk increases were detected for colorectal cancer during the 2nd to 5th year after the first PFTC diagnosis, for breast cancer in follow-up 10+ and for nonlymphoid leukaemia during the 2nd to 10th year. The clustering of cancers of the lung and bladder in PFTC patients may suggest shared smoking aetiology. The excess of colorectal and breast cancers after PFTC may indicate a genetic aetiology.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".