ENGAGING ADMINISTRATIVE DATA TO DETERMINE TIME TO DIAGNOSIS AND TREATMENT OF LYMPHOMA: A POPULATION BASED STUDY
Bibliographic record
Abstract
Introduction: The province of Manitoba (MB) has a goal of reducing time from suspicion of cancer to treatment to a target of 60 days. Most patients with suspicious symptoms present to primary care and referral is after diagnosis is confirmed. Time from suspicion to diagnosis, (diagnostic delay [DD]), is hypothesized to be inadequately captured in cancer centre records and we aimed to refine a method to identify milestones starting from initial health care contact to obtain baseline measures of delay. Methods: This study examined DD, treatment delay (TD) and system delay (SD) in patients (>17) diagnosed with B-cell lymphomas from 2005 to 2010 using administrative data (MB Cancer Registry, MB Health billing and Hospital Abstract data) and chart review of a random subset of patients. A triangulated data approach, using an iterative consultative process, identified events likely related to subsequent lymphoma diagnosis and milestones. By linking to referring provider, date of high suspicion (HS) was identified and intervals were calculated for DD, TD and SD. SD from the chart review and the algorithm was compared with quintile regression. Cumulative incidence curves of SD were generated assessing patient factors (age, gender, lymphoma subtype, stage, socioeconomic status) and system factors (route of HS, treatment type, continuity of care, region of residence). The difference between variables was tested using the log rank test with significance defined as p value ≤ 0.05. Results: The cohort included 1295 patients (51.7% male), median age 65 (17–100) with aggressive NHL (43.6%), indolent NHL (34%), HL (12%) and other B lymphomas (10.4%). Chart review was only able to identify HS in 22/112 patients and underestimated SD by a median of 22 days. A total of 297/1295 (22.9%) of patients have never been treated, with 6.1% (79/1295) and 10.5% (136/1295) due to death within 1 and 3 months of diagnosis, and remaining 12.4% (161/1295) alive without treatment to date. Overall, only 14.8% of patients met the target for SD with median SD 128 days (90%ile 324 days), DD 85 days (90%ile 278 days) and TD 41 days (90%ile 83 days). Lymphoma subtype (see Figure 1), age (older patients longer delays, p = 0.009), stage (stage IV shorter SD, p < 0.0001), route of HS (ER first presentation, p < 0.0001), first treatment type (chemotherapy shorter than radiation, p = 0.02) influenced SD whereas gender, socioeconomic status, place of residence, and continuity of primary care did not. Figure 1 Cumulative Time from High Suspicion (HS) to Treatment by Lymphoma Category Keywords: B-cell lymphoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".