A85 COLON CANCER SCREENING IN PATIENTS ASSESSED FOR LUNG TRANSPLANTATION
Bibliographic record
Abstract
Abstract Background Colorectal Cancer (CRC) mortality is significantly higher after a lung transplant (1). CRC screening for average risk patients in British Columbia is done with Fecal Immunochemical Testing (FIT) every two years (2), however colonoscopy is currently the standard modality in patients undergoing lung transplant assessments in British Columbia. The yield of using FITs or Fecal Occult Blood Testing (FOBT) and colonoscopy in screening for lung transplant assessments in Canada is unknown. Aims To review the colon cancer screening results for all lung transplants done in British Columbia from 2013 to 2018. Methods This is a retrospective chart review of the 222 lung transplants done from January 2013 to December 2019. Results 220 patients were transplanted during this time period. 2 patients were re-transplanted. 136 of the 220 lung transplant patients were male (62%). The most common indication for transplantation was interstitial lung disease (44%), followed by chronic obstructive pulmonary disease (30%), cystic fibrosis (7%), and pulmonary hypertension (4%). Colonoscopies were performed in 127 of the 220 patients. Computed tomography (CT) colonography was performed in 15 of the 220 patients, and a FOBT or FIT was performed in 200 of the 220 patients. No colon cancers were found by colonoscopy or CT colonography (0/142). Of the colonoscopies performed, 38 % (49/127) had adenomatous or serrated polyps removed. Of these 36% (18/49) had high risk pathology. The positive predictive value of a FIT/FOBT positive for a polyp was 51.4 % (CI 37.6–65.1%). Conclusions The value of non-invasive screening modalities in pre-lung transplant patients are modest. Program screening should be tailored to the lung transplant candidate’s risk of CRC and the risk of an invasive procedure with a known complication rate. Funding Agencies None
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".