Evaluating the endometabolic and bone health effects of Tyrosine Kinase Inhibitors in Chronic Myeloid Leukaemia: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Chronic Myeloid Leukaemia (CML) constitutes 15% of new adult leukaemia cases as well as 2%-3% of leukaemia in children under 15% and 9% of leukaemias in adolescents 15-19 years of age annually. The introduction of Tyrosine Kinase Inhibitors (TKI) therapy has dramatically improved survival in these patients, yet the off-target effects of this treatment may have long-term health impacts on CML survivors. The risk of adverse health outcomes is especially important in children, where TKI exposure may occur during critical windows of growth and puberty, and patients require treatment for prolonged periods of time. The aim of this systematic review protocol is to report on the methods used to conduct a systematic review to investigate the endometabolic and bone health effects of TKI therapy in CML. METHODS AND ANALYSIS: Searches will be conducted in the Cochrane Central Register of Controlled Trials, EMBASE and MEDLINE from inception on August 1st, 2019. Searches may be updated while performing the systematic review to ensure new evidence is included if applicable. Grey literature search will include ClinicalTrials.gov and ProQuest Dissertations and Theses A&I. We will perform a meta-analysis if there are at least two studies reporting similar populations, interventions, methods and tracking the same outcome measures. The studies should also have similar age and sex distributions. ETHICS AND DISSEMINATION: As this is a systematic review protocol, it does not include patient data; therefore, Research Ethics Board approval is not indicated. The systematic review will be published in a peer-reviewed journal and presented at international conferences. PROSPERO REGISTRATION NUMBER: CRD42018091175.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.062 | 0.085 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.016 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.062 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".