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Enregistrement W2512064859 · doi:10.1182/blood.v108.11.3322.3322

Trends in Outcomes and Quality of Care in Acute Myeloid Leukemia over Four Decades in Ontario, Canada.

2006· article· en· W2512064859 sur OpenAlexaffabout
Shabbir M.H. Alibhai, Marc Leach

Notice bibliographique

RevueBlood · 2006
Typearticle
Langueen
DomaineHealth Professions
ThématiqueHealthcare Systems and Practices
Établissements canadiensPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineReferralCancer registryPopulationLogistic regressionComorbidityCancerMyeloid leukemiaInternal medicineEmergency medicineDemographyPediatricsFamily medicineEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Abstract Acute myeloid leukemia (AML) is associated with a poor prognosis, particularly in patients age 60 or older, who comprise the largest group of patients with AML. However, most published data on AML outcomes are institution-based rather than population-based, and are associated with significant selection and referral biases. There is a paucity of data on quality of care in this population. Recent data from specialized centres have shown that with careful patient selection and aggressive chemotherapy, a significant number of older AML patients can achieve improved outcomes. This suggests that variations in outcomes may exist and may be due to differential rates of treatment, referral to specialized centres, or other aspects of quality of care. However, this issue has not been formally examined. We used the Ontario Cancer Registry, a comprehensive provincial cancer registry, to identify all patients diagnosed with AML in the province of Ontario between 1965 and 2003. Comorbidity was captured with the Charlson-Deyo Index. 30-day and one-year survival were examined across geographic region (8 regions), age group, and time using multivariable logistic regression. Referral to regional cancer centres (RCC) and receipt of chemotherapy were examined as quality of care indicators. A total of 9,365 patients (mean age, 58.1 y, range 0–103 y) were diagnosed with AML between 1965 and 2003. Mean age at diagnosis increased from 49.1 y in 1965 to 62.4 y in 2003. 53.3% of patients were male. There was a steady increase in the number of new cases per year that was greater than the population growth rate. Overall, 75.5% and 33.3% of patients survived to 30 days and one year. 30-day survival was 67.4% among patients age 60+ vs. 85.6% among age 19–59. One-year survival was also considerably lower at 20.3% vs. 49.2%. Both 30-day and one-year survival decreased per decade of age from age 19 onwards. Although 30-day and one-year survival improved over time among patients age 19–59, similar improvements were not seen in patients age 60+. Among patients age 60+, 30-day survival varied from 62.0% to 72.3% across regions, whereas one-year survival varied from 16.8% to 25.4%. The proportion of patients receiving chemotherapy declined with age (56.4% vs.28.0% among 19–59 vs. 60+ year olds). Similarly, significantly fewer patients age 60 or older were referred to a RCC (20.8% vs. 29.9%). Increasing age, increasing comorbidity, geographic region, lack of receipt of chemotherapy, and not being referred to a RCC were associated with greater 30-day mortality in multivariable models. Findings were similar for one-year survival although region was no longer a statistically significant predictor. The incidence of AML has been increasing over the last four decades, with a slight preponderance among males. The mean age at diagnosis has also slowly increased. Although the prognosis has improved over time among children and adults up to age 59, it remains poor among those age 60 or older. Clinically important differences in survival were seen across geographic regions. These differences were only partially explained by receipt of chemotherapy and referral to specialized cancer centres. More detailed clinical information is required to determine if opportunities exist to enhance the quality of care and thereby improve outcomes among older adults with AML.

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)0,003
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 enseignants0,043
Score d'incertitude au seuil0,311

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,006
Études des sciences et des technologies0,0010,001
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
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 Opus0,076
Tête enseignante GPT0,424
Écart entre enseignants0,348 · 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
Publié2006
Routes d'admission2
Résumé présentoui

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