A comparison of self-reported colorectal cancer screening with medical records.
Bibliographic record
Abstract
The purpose of this study was to compare self-reports of colorectal cancer (CRC) screening by fecal occult blood test (FOBT), sigmoidoscopy, and colonoscopy with medical records in a multiprovider health care setting. Relatives of CRC patients residing in Ontario, Canada completed a questionnaire indicating whether or not they had ever had any CRC screening tests. Medical records from physician's offices and hospitals were compared with the self reports, and where possible, reasons were obtained for nonmatching reports. Medical records for colonoscopies were readily available from various sources, and self-reports of this procedure were very accurate (kappa statistic for agreement beyond chance = 0.87). For sigmoidoscopy and FOBT, the agreement was poorer (kappa = 0.29 and 0.32, respectively); however, there were difficulties in obtaining records for these two procedures. Sigmoidoscopy procedures that took place many years ago were difficult to document, and physician's offices were unable to provide FOBT reports in many cases. Self-reports of colonoscopy were very accurate in this population, whereas self-reports of sigmoidoscopy and FOBT are somewhat less accurate, although this is likely due to challenges in obtaining a confirmatory record rather than an overreporting of tests. In a multiprovider publicly insured health care setting such as Canada, using self-reported information is likely to provide sufficiently accurate information for colonoscopy, but for other CRC screening tests, there may be difficulty in obtaining true estimates of the frequencies of these procedures.
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 | 0.008 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".