Determinants of delays in treatment initiation in children and adolescents diagnosed with leukemia or lymphoma in Canada
Bibliographic record
Abstract
Minimizing delays that may occur along the cancer care pathway requires an understanding of their determinants. Few studies on childhood cancers have been published on the factors that influence the time it takes for patients to get a first medical consultation (patient delay) and treatment (health care system [HCS] delay) once cancer symptoms have been recognized. Our objective was to assess factors related to disease, patient and HCS on patient and HCS delay for children and adolescents with leukemias and lymphomas in Canada. A prospective cohort study was conducted on subjects enrolled in the Treatment and Outcomes Surveillance program of the Canadian Childhood Cancer Surveillance and Control Program, a national surveillance program. We studied 963 leukemia and 397 lymphoma patients who were less than 19-years old at diagnosis in 1995-2000. Logistic regression models were used to measure the associations between candidate predictive factors and delays. Age was positively associated with patient delay for both leukemia and lymphoma patients, but not with HCS delay. Patients first seen in a hospital emergency room had a lower risk of HCS delay than patients first seen by a general practitioner. Cancer subtype was associated with patient delay for leukemia patients, and HCS delay for lymphoma patients. Longer patient delay was associated with a lower risk of long HCS delay for both cancers. Factors related to the patients, their disease and the HCS may exert different influences on varying segments of the care pathway of leukemia and lymphoma 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".