Timely access to care for colorectal cancer (CRC): A description and analysis of associated factors
Bibliographic record
Abstract
6028 Background: There are multiple points in the presentation, diagnosis and treatment of CRC where access to physicians and/or medical services may affect the timeliness of appropriate care. A single interval, such as time from diagnosis to surgery, may incompletely reflect the timeliness of care in CRC. We describe the various components of access to care for resectable CRC, and identify factors associated with variation in such components. Methods: From 02/15/2002 to 02/15/2004, all patients undergoing surgery for primary CRC within a single health district were enrolled in a prospective consecutive cohort study. A comprehensive, standardized method of ascertaining specific time intervals, including a patient interview, was used to quantify the timeliness of presentation, diagnosis and surgery. Differences in these time intervals according to demographic and clinical factors were examined using multivariate linear regression. Results: Among the 455 patients in the study cohort, the median time intervals (interquartile range) for the various components of access to care were as follows: symptoms to first physician visit - 36 days (12–79); first physician visit to diagnosis - 51 days (15–127); diagnosis to surgery - 23 days (10–39). On multivariate analysis, the interval from initial symptoms to first physician visit was 96% longer for patients < 50 years (p = 0.02) and 144% longer for rectal lesions (p < 0.001). The interval from first physician visit to diagnosis was 190% longer for patients < 50 years (p < 0.001), and was positively correlated with gross household income (p = 0.02). The interval from diagnosis to surgery was 33% longer in females (p = 0.003) and 70% longer for rectal lesions (p < 0.001). None of these intervals were found to vary significantly according to geographic residence, education level, body mass index, number of years with family doctor, smoking history, or previous use of CRC screening. Conclusions: The timeliness of access to care for CRC involves several defined components; the longest interval is from initial physician visit to diagnosis. Although associated factors vary somewhat according to the specific interval, interventions aimed at improving the timeliness of access to care in CRC should address younger patients and those with rectal cancer. No significant financial relationships to disclose.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".