Risk factors of skin tear in older persons: a protocol for systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Skin tear (ST) will prolong the hospitalisation time of an older person, increase the cost of medical expenses and the difficulty in care for nursing staff, and seriously affect the quality of life of the older person. Early identification and intervention of the elderly at risk of ST are key factors in preventing the occurrence of ST in older persons. At present, risk factors for ST in older persons have not been systematically evaluated, let alone summarised to analyse risk factors for ST in older persons. Therefore, this systematic review and meta-analysis aims to synthesise existing research on risk factors for ST in older populations. Methods and analysis The protocol is being reported by the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols statement. On 17 September 2023, we will start literature search in PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, Medline, Chinese Scientific Journal Database, Wan Fang Data Knowledge Service Platform, China National Knowledge Infrastructure, and Chinese Biomedical Literature Database. The language of the included literature is Chinese or English. Using RevMan V.5.4 software, we will perform a systematic review and meta-analysis of the final set of included studies to synthesise the data and draw meaningful conclusions. The Newcastle-Ottawa Quality Assessment Scale and the Agency for Healthcare Research and Quality will be used to assess the quality of the literature. The I 2 test will be used to test heterogeneity. Ethics and dissemination Ethical approval is not needed for this systematic review, as the study will not directly use information from human participants, and the data we use will be extracted from original studies. This systematic review and meta-analysis has been registered at the International Prospective Register of Systematic Reviews (PROSPERO). Once the systematic review and meta-analysis have been completed, we will publish our study in an academic journal. PROSPERO registration number CRD42023460810.
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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 | 0.082 | 0.130 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.031 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.005 |
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".