The impact of informing diagnosis on quality of life in patients with cancer
Bibliographic record
Abstract
BACKGROUND: Cancer is the second leading cause of death globally. More millions new cancer cases are diagnosed, and millions persons died due to cancer each year. There are different attitudes on disclosure of diagnosis to the patients with cancer. The current systematic reviews are qualitative, and indicate that there is limited evidence on the association between awareness of diagnosis and quality of life in patients with cancer. In this study, we aim to evaluate the effect of awareness of diagnosis on quality of life in patients with cancer by conducting a systematic review and meta-analysis. METHODS: We will perform a comprehensive electronic search in the databases below: MEDLINE (via PubMed), EMBASE, Cochrane Central Register of Controlled Trials, PsycINFO, WEB OF SCIENCE, Chinese Biomedical Literature database, WANFANG database, and China National Knowledge Infrastructure. The cohort studies focusing on the association between awareness of diagnosis and quality of life in patients with cancer will be included. The risk of bias for the included studies will be appraised using the Newcastle-Ottawa Scale tool for cohort study. We will pool the effect estimates from the included studies to quantitatively present the strength of the association of interest. RESULTS: This study will present pooled effect estimates regarding the impact of informing diagnosis on quality of life in patients with cancer. CONCLUSION: This is the first quantitative systematic review which tends to provide modest evidence on the association between informing diagnosis and quality of life in patients with cacner. PROSPERO REGISTRATION NUMBER: CRD42017060073.
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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 | 0.015 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".