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Compliance with Guidelines for Management of Chronic Myeloid Leukemia in Saskatchewan, Canada

2015· article· en· W2563459892 on OpenAlexaffabout
Dwip Maheshkumar Prajapati, Julie Stakiw, Mark Bosch

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsUniversity of SaskatchewanRoyal University Hospital
Fundersnot available
KeywordsMedicineMyeloid leukemiaClinical trialIntensive care medicineOncologyInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Tyrosine kinase inhibitors (TKIs) have revolutionized Chronic myeloid leukemia (CML) treatment by lowering the disease burden by providing more precise monitoring of response. However, review of new clinical trial data and updating management principles is essential to ensure continued optimal outcome for patients while minimizing treatment toxicity. The European Leukemia Net (ELN) has developed guidelines for CML management in 2006, 2009 with updates in 2013. They define optimal use of TKIs in first and second-line setting, best approach for monitoring and evaluating treatment responses, and evidence based management interventions for suboptimal responses. One study has shown achievement of Major Molecular Response (MMRs) at 12 months, compared with the lack of MMRs at this time point, had been associated with superior event-free survival and lack of progression to the accelerated or acute phase (Hughes TP, et al.). German CML Study IV supports these results as well. Even with this evidence, data suggest that 20% to 30% patients with CML are not being treated according to current CML guidelines (Quintas-Cardama A, et al). This retrospective chart review will compare management of CML patients in Saskatchewan, Canada with the suggested ELN guidelines, specifically looking for changes in management for suboptimal responses or patient toxicity, and will determine if Saskatchewan patient management requires further optimization. Methods: The charts of CML patients from Saskatchewan Cancer Centre were retrospectively reviewed from January 2009 to December 2014. For each patient the following information was extracted: date of diagnosis, initial treatment and start date, response to treatment and any change in treatment based on test results as suggested by the ELN guidelines. Timing of treatment response evaluation as per ELN guidelines (1 month after treatment initiation followed by molecular testing at 3 month intervals), and interventions for suboptimal responses were recorded. Results: 58 patients were diagnosed with CML in the study period. 23 (41%) patients were no longer on frontline TKI therapy and representing either failure of Imatinib (69.57%) or intolerance to Imatinib (30.43%). 24 (41.38%) patients were managed in accordance with ELN guidelines. 27(46.55%) patients were not managed according to ELN guidelines (Figure-1). Non-compliance varied from minor deviations with delays in monitoring response to therapy in 12 (20.69%) to major omissions such as not changing treatment based on suboptimal responses in 12 (20.69%) patients. In 11 (19%) patients, important milestone responses required for optimal patient outcome were not met and no change in therapy was evident. Most importantly, treatment failure as defined in the ELN guidelines, without a change in management occurred in 4 (7%) patients. Conclusion: This study demonstrates that management of CML at Saskatchewan Cancer Centre, according to ELN guidelines, occurs in less than 50% of patients. Most surprisingly, 7% of patients were defined as having failed treatment and in need of treatment change to prevent progression of their disease. This review suggests that management of CML patients in Saskatchewan requires further evaluation and potential interventions put in place to ensure optimal CML patient outcomes. The ELN guidelines are recognized worldwide to be the most up to date and evidence based guidelines for optimal CML patient management. Therefore, following CML treatment outcome should be a standard Hematology matrix, thereby creating treatment centres of excellence. Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.

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)0.005
metaresearch head score (Gemma)0.025
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.299

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.009
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.004

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 head0.061
GPT teacher head0.306
Teacher spread0.245 · 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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Citations1
Published2015
Admission routes2
Has abstractyes

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