Examining the pathway to diagnosis and treatment of lymphoma in Manitoba: patient and system factors resulting in delay
Bibliographic record
Abstract
The province of Manitoba has set a goal of reducing time from suspicion of cancer to treatment to a target of sixty days. To attain this goal, a baseline understanding of current time intervals is required. This study examined system, diagnostic and treatment delay in adult patients (> 17) diagnosed with Lymphomas from 2005 to 2010 using administrative data (Manitoba Cancer Registry, Manitoba Health billing data and Hospital Abstract data) and a chart review of a random subset of patients. A triangulated data approach was used to identify suspicion of lymphoma and milestones in the patient journey and to measure delays in diagnosis and treatment. Using an iterative consultative process, an algorithm was built to identify index events likely related to subsequent lymphoma diagnosis. Then, claims data was searched for a referring provider for each index event. The last visit with a referring provider, prior to the first index event, was selected as date of high suspicion. The study found that 14.8% of patients met the provincial target of less than sixty days from suspicion to treatment. Median time from high suspicion to treatment, referred to as system delay, was 128 days and the median time from diagnosis to treatment was 41 days. Time to diagnosis accounted for two thirds of system delay. In conclusion, this study demonstrated the merit of a triangulated approach. As well the clinical pathway developed and the target timelines for milestones have operational value and can be used to direct process improvements to shorten delays for future patients.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".