Quantifying Treatment Delays in Adolescents and Young Adults with Cancer at McGill University
Bibliographic record
Abstract
BACKGROUND: Since the end of the 1980s, the magnitude of survival prolongation or mortality reduction has not been the same for adolescents and young adults (ayas) with cancer as for their older and younger counterparts. Precise reasons for those observations are unknown, but the differences have been attributed in part to delays in diagnosis and treatment. In 2003 at the Jewish General Hospital, we developed the first Canadian multidisciplinary aya oncology clinic to better serve this unique patient population. The aim of the present study was to develop an approach to quantify diagnosis delays in our aya patients and to study survival in relation to the observed delay. METHODS: In a retrospective chart review, we collected information about delays, treatment efficacy, and obstacles to treatment for patients seen at our aya clinic. RESULTS: From symptom onset, median time to first health care contact was longer for girls and young women (62 days) than for boys and young men (6 days). Median time from symptom onset to treatment was 173 days; time from first health care contact to diagnosis was the largest contributor to that duration. Delays in diagnosis were shorter for patients who initially presented to the emergency room, but compared with patients whose first health contact was of another type, patients presenting to the emergency room were 3 times more likely to die from their disease. CONCLUSIONS: Delays in diagnosis are frequently reported in ayas with cancer, but the duration of the delay was unrelated to survival in our sample. Application of this approach to larger prospective samples is warranted to better understand the relation between treatment delay and survival in ayas-and in other cancer patient groups.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".