Determining the cancer diagnostic interval using administrative data in a cohort of patients with pancreatic ductal adenocarcinoma (PDAC).
Bibliographic record
Abstract
e13551 Background: PDAC is a leading cause of cancer death that is often diagnosed at an advanced stage. Population-based administrative data can be a valuable resource for studying the diagnostic interval, defined as the time from the first related healthcare encounter to cancer diagnosis. The objective of this study was to determine the diagnostic interval in a cohort of patients with PDAC using an empirical approach. Methods: This is a retrospective, cohort study of patients diagnosed with PDAC from 2007 – 2015 in Alberta, Canada. We used the Alberta Cancer Registry, physician billing claims, hospital discharge and emergency room visits to identify and categorize cancer-related healthcare encounters before diagnosis. We used statistical control charts to define the lookback period for each encounter category and used these lookback periods to identify the earliest encounter that represented the start of the diagnostic interval (index contact date). The end of the interval was the diagnosis date. Quantile regression was used to determine factors associated with the diagnostic interval. Results: We identified 3,142 patients with PDAC. Median age of diagnosis was 71 (IQR 61-80). We identified an index contact date and thus a diagnostic interval in 96.5% of patients. The median diagnostic interval length was 76 days (IQR 21-191; 90th percentile 276 days). A higher Elixhauser comorbidity score (+18.57 days/ 1 point increase, 95% CI 16.07-21.07, p<0.001) and stage 3 disease compared to stage 2 disease (+22.55 days, 95% CI 5.02-40.08, p=0.01) were associated with a longer diagnostic interval. Conclusions: In this cohort of patients with PDAC, there was a wide range in the diagnostic interval with 10% of patients having a diagnostic interval of approximately 9 months. Diagnostic interval research using administrative databases can understand variations in diagnosis times and can inform early detection efforts by identifying where and in whom delays may occur.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".