Incidence of constipation and associated factors in the period of lockdown during COVID-19 pandemic: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction The lifestyle and habit changes that have emerged as a result of quarantine measures may have had a negative impact on defecation habits. However, there is a lack of data on combined estimates of its occurrence and prevalence. Methods and analysis We will conduct a systematic search for observational studies on PubMed/MEDLINE, Web of Science, Cochrane Library, EMBASE, CNKI, SinoMed, VIP China Science and Technology Journal database, Chinese Biomedical Databases and Wanfang Data. The search will include literature published from the inception of the databases to September 2022. Two authors will independently screen articles and extract data based on predefined inclusion and exclusion criteria. The risk of bias in the included studies will be evaluated using the Newcastle-Ottawa Scale for observational studies. Statistical analysis will be performed using Review Manager software V.5.4 and STATA V.16.0 software. Heterogeneity among studies will be assessed using the Q statistical test and I 2 statistical tests. In case of significant heterogeneity, subgroup analysis and sensitivity analysis will be conducted to explore the source of heterogeneity. Sensitivity analyses will also be performed to assess the reliability of the study findings. If feasible, a meta-analysis will be conducted. Otherwise, a descriptive synthesis will be performed using a best-evidence synthesis approach. The primary outcome of interest will be the prevalence of constipation. The secondary outcomes will involve examining the association of risk factors. To evaluate potential publication bias, we will use both the Begg funnel plot and Egger’s weighted regression statistics. Furthermore, to accurately assess the quality of evidence for our primary outcome, we will employ the Grading of Recommendations Assessment, Development and Evaluation system. Ethics and dissemination This systematic review protocol will only consider published studies available in databases and will not include individual patient data. Therefore, ethical approval is not required, and the findings will be published in a peer-reviewed journal. PROSPER registration number CRD42022366176.
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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 | 0.072 | 0.080 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.023 | 0.036 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 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".