Cancer and suicidal ideation and behaviours: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Prevalence of suicidal ideation (SI) and behaviours are higher among patients with cancer than general population. No systematic review/meta-analysis investigated this topic; therefore, our aim will be to assess the relationship between cancer and SI and behaviours. METHODS: We will search PubMed/MEDLINE, EMBASE, SCOPUS, Web of Science, PsycINFO and Cochrane Library databases from their inception until 30 June 2018. Case-control and cohort studies focused on the association between cancer (any type) and suicidal outcomes (suicide, suicide attempt and SI) will be included. Two team members will independently: (A) perform the selection of the included studies and data extraction, with the supervision of a third member in case of discrepancies and (B) assess each study with: (1) Newcastle-Ottawa Scale (NOS); (2) Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement; (3) Grading of Recommendations Assessment, Development and Evaluation (GRADE). We will conduct a random-effects meta-analysis. Individual and pooled ORs and associated 95% CIs will be calculated as well as between-study heterogeneity. We will examine the potential for publication bias. If possible, we will explore reasons for potential between-study heterogeneity. ETHICS AND DISSEMINATION: This study does not require ethical approval. The study will be submitted to a peer-reviewed journal, will be publicly disseminated and will be the topic of research presentations. PROSPERO REGISTRATION NUMBER: CRD42017072482.
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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 | 0.080 | 0.109 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.091 | 0.011 |
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".