Determining the Cancer Diagnostic Interval Using Administrative Health Care Data in a Breast Cancer Cohort
Bibliographic record
Abstract
PURPOSE: Population-based administrative health care data could be a valuable resource with which to study the cancer diagnostic interval. The objective of the current study was to determine the first encounter in the diagnostic interval and compute that interval in a cohort of patients with breast cancer using an empirical approach. METHODS: This is a retrospective cohort study of patients with breast cancer diagnosed in Ontario, Canada, between 2007 and 2015. We used cancer registry, physician claims, hospital discharge, and emergency department visit data to identify and categorize cancer-related encounters that were more common in the three months before diagnosis. We used statistical control charts to define lookback periods for each encounter category. We identified the earliest cancer-related encounter that marked the start of the diagnostic interval. The end of the interval was the cancer diagnosis date. RESULTS: The final cohort included 69,717 patients with breast cancer. We identified an initial encounter in 97.8% of patients. Median diagnostic interval was 36 days (interquartile range [IQR], 19 to 71 days). Median interval decreased with increasing stage at diagnosis and varied across initial encounter categories, from 9 days (IQR, 1 to 35 days) for encounters with other cancer as the diagnosis to 231 days (IQR 77 to 311 days) for encounters with cyst aspiration or drainage as the procedure. CONCLUSION: Diagnostic interval research can inform early detection guidelines and assess the success of diagnostic assessment programs. Use of administrative data for this purpose is a powerful tool for improving diagnostic processes at the population level.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".