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Record W3212528433 · doi:10.1182/blood-2021-147326

Rates and Predictors of Visits to Primary Care Physicians during and after Treatment of Childhood Acute Lymphoblastic Leukemia: A Population-Based Cohort Study

2021· article· en· W3212528433 on OpenAlexaffabout
Vicky R. Breakey, Paul C. Nathan, Serina Patel, Laura Wheaton, Li Q, Rinku Sutradhar, Mylène Bassal, Paul Gibson, Jason D. Pole, Uma H. Athale, Sumit Gupta

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsKingston General HospitalChildren's Hospital of Eastern OntarioInstitute for Clinical Evaluative SciencesHospital for Sick ChildrenMcMaster UniversityMcMaster Children's HospitalLondon Health Sciences CentreUniversity of Toronto
Fundersnot available
KeywordsMedicinePopulationCohortSurvivorship curveSocioeconomic statusPediatricsFamily medicineInternal medicine

Abstract

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Abstract Introduction: Though ideal models of survivorship care are not definitively established, it has been suggested that childhood acute lymphoblastic leukemia (ALL) survivors can be cared for by properly informed primary care physicians (PCP - e.g. family physicians, community pediatricians) given low risks of late effects. PCP-driven models of care are dependent on the willingness of families to re-engage with their PCPs after a prolonged period of treatment delivered by pediatric oncologists during which PCP involvement may be minimal. We thus aimed to identify rates and predictors of PCP visits both during and after treatment among a population-based cohort of children with ALL. Methods: We identified all children <18 years at diagnosis of ALL at a pediatric center in Ontario, Canada between 2002-2012. Patients were linked to healthcare data and matched to population-controls by age, sex, and geography (1:5 ratio). PCP physicians at the time of diagnosis were identified through validated algorithms using primary care billing codes and patient rosters. PCP visit rates during treatment were determined and compared between cases and controls, with cases censored at the time of relapse, stem cell transplant, or death. Post-treatment PCP visit rates were calculated among those completing frontline therapy until relapse, death, or the end of the study period. Predictors included demographic- (e.g. age, sex, socioeconomic status, distance from treatment center), disease-related (e.g. risk status, lineage), and PCP-related variables (e.g. pediatrician vs. non-pediatrician). Results: Of 801 children with ALL, 751 (93.8%) had an identified PCP at the time of diagnosis. Excluding a further 8 (1.0%) patients who did not have treatment information available resulted in 743 cases and 3,112 controls. The median age of children with ALL was 4.0 years [interquartile range (IQR) 3.0-8.0], 409 (55%) of whom were male. Nearly half of patients (361, 48.6%) did not visit their PCP during treatment. The rate of PCP visits during treatment was 0.64 per person per year (PPPY) compared to 1.4 PPPY among controls. Adjusting for age, sex, and socioeconomic status resulted in an adjusted rate ratio (aRR) of 0.47 [95th confidence interval (95CI) 0.40-0.54; p<0.0001]. In multivariable analyses, no disease-related factors were associated with PCP visit rates. Infants had lower PCP visit rates during treatment (RR 0.09 vs. age 1-4 years, 95CI 0.01-0.6, p=0.01) while patients living at greatest distance from their treatment centre had higher rates (RR 1.6, 95CI 1.1-2.3, p=0.01). PCP type (pediatrician vs. other) did not have an impact on visit rates during treatment. Excluding 32 (4.3%) patients who relapsed, died, or underwent stem cell transplant prior to completing frontline therapy yielded 711 cases (survivors) and 2,973 controls available for analyses of post-treatment PCP visits. The median time of follow up after the end of treatment among survivors was 6.0 years (IQR 4.0-8.5). Though 287 (40.4%) of survivors did not visit their PCP during the post-treatment period, survivors overall still experienced greater post-treatment PCP visit rates compared to controls (aRR 1.4, 95CI 1.2-1.6; p<0.0001). This was true throughout the post treatment period, with the greatest increase in visit rates compared to controls seen 10 years from the end of treatment and beyond (aRR 3.6, 95CI 1.7-7.5, p=0.0007). In multivariable analyses, survivors who had seen their PCP during active treatment had post-treatment visit rates twice as high as those who had not (aRR 2.0, 95CI 1.6-2.5; p<0.0001). Survivors with pediatricians as PCPs also had higher post-treatment visit rates compared to survivors who did not (aRR 1.4, 95CI 1.1-1.8; p=0.003). Conclusions: Only a portion of children with ALL see their PCPs during treatment and return to PCP care following the completion of leukemia treatment, indicating that PCP-led survivorship care is feasible only for a subset of this population. The rate of PCP visits among survivors however continues to increase relative to general-population controls beyond 10 years after end of treatment. Post-treatment engagement with PCPs may be improved by PCP involvement during treatment, as well as in some cases the involvement of community pediatricians. Disclosures Gupta: Jazz Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.454

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.246
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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