Risk factors for pulmonary infection in elderly patients with type 2 diabetes: A protocol for systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Lung infection is a prevalent chronic consequence of diabetes. Abnormal blood sugar levels, vascular endothelial damage, and alterations in capillary permeability predispose diabetes patients to lung infections. Currently, there is no comprehensive review addressing the risk factors for lung infection in diabetes. Consequently, our objective is to conduct a systematic review of the existing risk factors for lung infection in diabetes and offer recommendations for the targeted enhancement of treatment strategies. METHODS AND ANALYSIS: We will search five English literature databases (PubMed, Embase, Web of Science, CINAHL, and Cochrane Library) and 4 Chinese databases (CNKI, WanFang, SinoMed and VIP) since the founding of the database until December 01, 2024. We will perform a systematic examination and meta-analysis of cohort, case-control and cross-sectional studies to identify all population-based risk factors for diabetes patients with pulmonary infection. Two researchers will independently assess the publication, extract data, and evaluate the quality and potential biases present in the study. We will utilize RevMan 5.4 software and STATA 16.0 for data analysis. The included studies will be assessed using the Newcastle Ottawa Quality Assessment Instrument (NOS) and Agency for Healthcare Research and Quality (AHRQ). If the heterogeneity of the included studies is excessively high, we will perform subgroup and sensitivity analysis to identify probable sources of heterogeneity. The assessment of publication bias will be conducted using a funnel plot. Furthermore, we will employ the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the quality of evidence for each exposure and outcome of interest. DISCUSSION: This article introduces a research protocol to explore the influencing factors of pulmonary infection in diabetes. The results of this study will summarize the evidence of influencing factors of pulmonary infection in diabetes at present. We hope to provide reliable advice for clinicians to make decisions, so as to support the implementation of effective prevention strategies for diabetes pulmonary infection. TRIAL REGISTRATION: PROSPERO CRD42024606429.
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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 | 0.058 | 0.087 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 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".