Rates and predictors of visits to primary care physicians during and after treatment of childhood acute lymphoblastic leukemia: A population‐based cohort study
Bibliographic record
Abstract
INTRODUCTION: Patient re-engagement with primary care physicians (PCPs) after cancer treatment is essential to facilitate survivorship care and to meet non-oncology primary care needs. We identified rates and predictors of PCP visits both during and after treatment among a population-based cohort of children with acute lymphoblastic leukemia (ALL). METHODS: Children of age less than 18 years at ALL diagnosis in Ontario between 2002 and 2012 were linked to administrative data and matched to controls without cancer. PCPs at diagnosis were identified and PCP visit rates during treatment compared between patients and controls. Post-treatment PCP visit rates were also calculated. Predictors included demographic-, disease-, and PCP-related variables. RESULTS: A total of 743/793 (94%) patients and 3112/3947 (79%) controls had a PCP at diagnosis. Almost half of patients (361/743, 45%) did not visit their PCP during treatment. Visit rate during treatment was 0.64 per person per year (PPPY) versus 1.4 PPPY among controls (adjusted rate ratio [aRR] 0.47, 95th confidence interval [95CI]: 0.40-0.54; p < .0001). No disease- or PCP-related factors were associated with visit rates. Total 711 patients completed frontline therapy; 287 (40.4%) did not have a PCP visit after treatment. Nonetheless, survivors overall visited PCPs post treatment more often than controls (aRR 1.4, 95CI: 1.2-1.6; p < .0001). Survivors who saw their PCP during treatment had post-treatment visit rates twice that of other survivors (aRR 2.0, 95CI: 1.6-2.5; p < .0001). CONCLUSIONS: Only a portion of children with ALL see their PCPs during treatment and return to PCP care following treatment completion. Post-treatment engagement with PCPs may be improved by PCP involvement during ALL treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".