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Enregistrement 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 sur 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

Notice bibliographique

RevueBlood · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueChildhood Cancer Survivors' Quality of Life
Établissements canadiensKingston General HospitalChildren's Hospital of Eastern OntarioInstitute for Clinical Evaluative SciencesHospital for Sick ChildrenMcMaster UniversityMcMaster Children's HospitalLondon Health Sciences CentreUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicinePopulationCohortSurvivorship curveSocioeconomic statusPediatricsFamily medicineInternal medicine

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,229
Score d'incertitude au seuil0,454

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,005
Tête enseignante GPT0,246
Écart entre enseignants0,240 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission2
Résumé présentoui

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