Pancreatic cancer progression and mortality predicted by depression and anxiety: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
Although the relationship between psychological factors and pancreatic cancer outcomes has been widely discussed, controversy remains. We will for the first time systematically summarize the literature to explore the correlation of anxiety and depression to the prognosis of patients with pancreatic cancer. The findings will fill existing research gaps, informing healthcare providers about better psychological care and medical treatment. The following databases will be retrieved from their inception to July 2023: Cochrane Library, MEDLINE (PubMed), Web of Science, EMBASE, and four Chinese databases (Chinese National Knowledge Infrastructure, Wanfang Database, Chinese Biomedical Literature Database, and Chongqing VIP Chinese Science and Technology Periodical Database). The World Health Organization Clinical Trials Registry, Chinese Clinical Registry, and ClinicalTrials.gov will be searched to identify other related studies. A manual search will be performed to identify missing eligible studies based on the reference list of selected articles. The search will focus on studies published in Chinese or English. To assess the risk of bias in the selected articles, Newcastle-Ottawa Quality Assessment Scale (NOS) will be used for the cohort study. Funnel plots and Egger's test will be used to assess whether publication bias exists. Moreover, the Grading of Recommendations Assessment Development and Evaluation (GRADE) will be utilized to analyze the credibility of the results from selected articles. Two independent evaluators will implement the study selection and data extraction, as well as evaluate the risk of bias and evidence quality. Data will be analyzed using Stata 16.0. Trial registration: PROSPERO registration number is CRD42022366232.
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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 | 0.053 | 0.062 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.025 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.066 | 0.006 |
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".