Evaluating the prevalence of diabetes mellitus subtypes in childhood cancer survivors: a systematic review protocol
Bibliographic record
Abstract
Sondra Song Jie Chen,1,2 Ajantha Nadarajah,1,2 Laura Banfield,3 Adam Fleming,1,4 Lehana Thabane,5–8 Carol Portwine,1,4 M Constantine Samaan1–2,51Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada; 2Division of Pediatric Endocrinology, McMaster Children’s Hospital, Hamilton, Ontario, Canada; 3Health Sciences Library, McMaster University, Hamilton, Ontario, Canada; 4Division of Pediatric Hematology/Oncology, McMaster Children’s Hospital, Hamilton, Ontario, Canada; 5Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada; 6Department of Anesthesia, McMaster University, Hamilton, Ontario, Canada; 7Centre for Evaluation of Medicines, St Joseph’s Health Care, Hamilton, Ontario, Canada; 8Biostatistics Unit, St Joseph’s Healthcare, Hamilton, Ontario, CanadaObjectives: The number of children who survive cancer is reaching new record levels, thanks to improved management strategies. However, this population is predisposed to chronic health conditions including cardiovascular disease and type 2 diabetes, yet the full scale of these diagnoses in this population is unclear. This protocol describes the conduct of a systematic review to report on the prevalence of diabetes mellitus (DM) subtypes in childhood cancer survivors.Methods: Searches will be conducted in MEDLINE, Embase, CINAHL, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials. We will also search gray literature in Theses A&I, ProQuest Dissertations, and Web of Science as well as clinicaltrials.gov. Screening search results and data abstraction will be done independently by two reviewers. We will conduct a meta-analysis if two studies have similar designs, populations, methods, and outcome measures reported.Results: The findings of this systematic review will provide insights into the scale of diabetes in childhood cancer survivors to allow the prioritization of subpopulations that need specific interventions to screen, prevent, and treat DM. This will likely lead to improved outcomes in childhood cancer survivors.Keywords: systematic review protocol, childhood cancer, cancer survivorship, diabetes mellitus, type 1 diabetes mellitus, type 2 diabetes mellitus
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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 | 0.036 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.003 |
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".