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

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

Bibliographic record

VenueBlood · 2006
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineReferralCancer registryPopulationLogistic regressionComorbidityCancerMyeloid leukemiaInternal medicineEmergency medicineDemographyPediatricsFamily medicineEnvironmental health

Abstract

fetched live from 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.

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)0.003
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 score0.043
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.006
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.076
GPT teacher head0.424
Teacher spread0.348 · 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".

Quick stats

Citations0
Published2006
Admission routes2
Has abstractyes

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