Bibliographic record
Abstract
ObjectiveTo systematically evaluate the relationship between angiotensin-converting enzyme inhibitors(ACEI) and the risk for falls in the elderly.MethodsCase-control studies and cohort studies of relationship between ACEI and risk for falls in the elderly were retrieved from PubMed,EMbase,CENTRAL,Elsevier,China National Knowledge Infrastructure(CNKI),China Biomedical Literature Database(CBM),VIP Journal Integration Platform(VJIP),and Wanfang Database from the establishment to December 2017. The Newcastle-Ottawa Scale(NOS) were used for quality evaluation,and meta-analysis was performed by using RevMan 5.3 software.ResultsA total of 5 case-control studies and 6 cohort studies were included,involving 69 870 patients.Meta-analysis results showed that:①in case-control studies,combined results of uncorrected OR and corrected OR showed that ACEI increased the risk for falls in the elderly[uncorrected OR=1.29,95% <italic>CI</italic> (1.24,1.36),<italic>P</italic><0.05;corrected OR=1.09,95% <italic>CI</italic> (1.04,1.14),<italic>P</italic><0.05];②in cohort studies, combined results of uncorrected OR and corrected OR showed that ACEI did not increase the risk for falls in the elderly [uncorrected OR=1.03,95% <italic>CI</italic> (0.79,1.33),<italic>P</italic>=0.84;corrected OR=0.98,95% <italic>CI</italic> (0.86,1.12),<italic>P</italic>=0.77];③Meta-analysis results showed that ACEI would not increase the risk for falls in the elderly whether under different research locations or comprehensive controlled confounding factors(<italic>P</italic>>0.05).ConclusionsThe results of the meta-analysis based on cohort study and the results of different research locations and comprehensive control of confounding factors suggest that ACEI does not increase the risk for falls in the elderly.In view of the limitations of the quantity and quality of the included studies,the results need further verified by more rigorous clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.280 | 0.004 |
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; both teacher heads agree on what is shown here.
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".