Use of population-based electronic databases for the identification of patients with synchronous colorectal cancer and liver metastases potentially eligible for a surgical trial
Bibliographic record
Abstract
BACKGROUND: Some population-based recruitment methods, such as registries and databases, have been used to increase enrolment in clinical trials by identifying eligible participants based on baseline characteristics; however; these methods have not been tested in surgical trials, in which accrual occurs before surgery. We evaluated the use of population-based electronic databases to identify patients who potentially could be accrued to the Simultaneous Resection of Colorectal Cancer with Synchronous Liver Metastases (RESECT) trial and compared it to the traditional methods used to accrue patients (e.g., multidisciplinary rounds, letters to community surgeons) for that same trial during the same period. METHODS: An electronic database (ePath) was interrogated every 2 weeks for patients diagnosed with colorectal cancer from Feb. 1, 2017, to Mar. 30, 2019. A radiologic image database (OneView) was reviewed to identify those with liver metastases (level 1 screening). Reports were interrogated to identify potentially eligible patients for the RESECT trial (level 2 screening). A hepatobiliary surgeon reviewed radiology images to identify eligible patients for the trial (level 3 screening). The primary outcome was patient eligibility for the ongoing RESECT trial. RESULTS: The population-based method identified 90 (11.2%) of 803 patients diagnosed with colorectal cancer over the study period. Among the 90 patients, level 2 screening identified 60 (67%) potentially eligible patients for the RESECT trial. Of the 90 patients, 18 (20%) were eligible after radiographic image review (level 3 screening). Traditional accrual methods identified 38 patients with liver metastases, 27 (71%) of whom were identified as potentially eligible on level 2 screening, and 14 (37%) of whom were deemed to be eligible on level 3 screening. Twenty-six patients were identified by both methods. Twelve patients were identified by population-based methods alone, and 8 patients by traditional methods alone. Six eligible patients were identified by both methods. Baseline characteristics were similar between the 2 groups. CONCLUSION: A population-based electronic database method of patient accrual was able to identify eligible participants for the RESECT trial. However, optimal accrual likely requires the use of traditional methods as well.
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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 | 0.035 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".