Impact of prior malignancies on outcome of colorectal cancer; revisiting clinical trial eligibility criteria
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Most clinical trials on colorectal cancer (CRC) exclude cases who have history of a prior malignancy. However, no prior research studied this history's actual impact on the survival of CRC. In the paper, we study the effects of having a malignancy preceding CRC diagnosis on its survival outcomes. METHODS: CRC patients diagnosed during 1973-2008 were reviewed using the SEER 18 database. We calculated overall survival and cancer-specific survival of subsequent CRC, and more specifically stage IV CRC, using Kaplan-Meier test and adjusted Cox models. RESULTS: A total 550,325 CRC patients were reviewed, of whom 31,663 had history of a prior malignancy. The most commonly reported sites of a prior malignancy were: prostate, breast, urinary bladder, lung, and endometrium. Patients with history of a prior non-leukemic malignancy or history of a prior leukemia were found to have worse overall survival (HR = 1.165 95%CI = 1.148-1.183, P < 0.001) and (HR = 1.825 95%CI = 1.691-1.970, P < 0.001), respectively. However, CRC patients with history of a prior non-leukemic malignancy showed an improved colorectal cancer-specific survival (HR = .930 95%CI = .909-.952, P < 0.001). Analysis of stage IV CRC patients showed that patients with history of any non-leukemic malignancy did not have a significant change in overall survival. Whereas, patients with a prior leukemia showed a worse overall survival (HR = 1.535, 95%CI = 1.303-1.809, P < 0.001). When analyzed separately, right CRC and left CRC showed similar survival patterns. CONCLUSION: A prior malignancy before CRC -in general- can be associated with worse clinical survival outcomes. These worse outcomes are not observed in stage IV CRC. Considering these results when including/excluding stage IV CRC patients with prior malignancies in clinical trials may play help improve their generalizability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it