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Adherence to Chronic Myeloid Leukemia Monitoring and Treatment Guidelines in Canadian Registries

2016· article· en· W2613372458 on OpenAlexaffabout
Christopher Hillis, Lambert Busque, Julie Stakiw, Donna L. Forrest

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsBC Cancer AgencySaskatchewan Cancer AgencyUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalHôpital Maisonneuve-RosemontMcMaster University
Fundersnot available
KeywordsMedicineImatinib mesylateDisease registryImatinibFamily medicineMyeloid leukemiaDasatinibCancer registryClinical trialCancerInternal medicineDisease

Abstract

fetched live from OpenAlex

Abstract Background: Registry data in chronic myeloid leukemia (CML) complement clinical trial data, and can help determine how closely real world clinical practice adheres to guidelines. Several reports addressing this issue have suggested adherence to monitoring guidelines varies. However, no Canadian data on this topic has been published to date. To provide insight into this issue, we present data from the British Columbia (BC), Saskatchewan (SK), Ontario (ON) and Quebec (QC) CML registries. Methods: Data on cytogenetic and molecular monitoring were analyzed for CML patients treated with first-line imatinib from 2001-2015 in the BC registry, 2009-2014 in the SK registry and 2001-2014 in the ON registry. From 2006, clinicians in BC and SK were advised to follow the European LeukemiaNet (ELN) monitoring recommendations. Molecular monitoring of BCR-ABL for these provinces was conducted at the BC Cancer Agency Molecular Genetics Laboratory according to standard practices. In ON, clinicians were not advised to follow any particular guidelines and molecular and cytogenetic tests were conducted by the Hamilton Regional Laboratory Medicine Program using contemporary standards. In QC, province-specific guidelines were in place beginning in 2012 (see www.gqr-lmc-nmp.ca for specific guidance). Treatment patterns for patients treated with first-line imatinib from BC, SK and QC were analyzed for the 2001-2015, 2001-2014 and 2002-2012 time periods, respectively. Results: Monitoring data were collected for 234, 58 and 104 patients from BC, SK and ON, respectively. As shown in table 1, adherence to monitoring recommendations in Canada was 70% to 80% at 12 months. Treatment data were available for 234 BC patients, 73 SK patients, and 223 QC patients. Data on adherence to treatment recommendations were available for 58 SK patients diagnosed with CML and treated with first-line imatinib between 2009 and 2014. Of these 58 patients, over a quarter (n=15) experienced treatment failure or failed to meet ELN milestones without a change in therapy. Smaller proportions of patients receiving first-line imatinib therapy in BC and QC remained on imatinib therapy (see table 2). Discussion and Conclusions: These data suggest there is room for improvement with regards to adherence to CML monitoring and treatment recommendations in Canada. However, assessment of adherence to recommendations and inter-provincial comparisons are limited by the fact that monitoring and treatment guidelines have evolved over the data collection time period, as well as by differences in data collection strategies. For instance, in the ON registry, monitoring at the 3-month time point may be lower as testing was not typically conducted at 3 months in ON during the early 2000s. The opposite pattern observed in BC (with higher testing rates at 3 months dropping off by 18 months) may be attributable to the strict time period definition, with more patients receiving testing outside of the 4-week window after 1 year or more on treatment. In spite of these limitations, data collection through these registries continues to improve our understanding of real world CML populations and its management in Canada, as well as to spur initiatives aimed at improving CML care. This study was sponsored by Bristol-Myers Squibb. Professional medical writing and editorial assistance was provided by MedPlan Communications Inc. and was funded by Bristol-Myers Squibb. Disclosures Hillis: Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau; BMS: Honoraria; Celgene: Consultancy. Busque:Novartis: Honoraria, Research Funding, Speakers Bureau; BMS: Honoraria, Speakers Bureau; Pfizer: Honoraria, Speakers Bureau. Stakiw:Roche: Research Funding; BMS: Honoraria; Novartis: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau; Celgene: Honoraria, Speakers Bureau; Jansen: Honoraria, Speakers Bureau. Forrest:BMS: Consultancy, Research Funding; Ariad: Honoraria, Speakers Bureau.

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)0.047
metaresearch head score (Gemma)0.158
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 score0.944
Threshold uncertainty score0.403

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.158
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.018
Science and technology studies0.0030.001
Scholarly communication0.0040.001
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.307
Teacher spread0.268 · 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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Citations2
Published2016
Admission routes2
Has abstractyes

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