Methods for chronic wound research — A scoping systematic review of the recommendations, guidelines and standards
Bibliographic record
Abstract
Background This scoping systematic review aimed to investigate the existing literature for recommendations, guidelines and standards for research on chronic wound diagnosis, assessment, management and prevention; to identify gaps in this literature; and produce recommendations to support future wound management research. Methods for chronic wound research -A scoping systematic review of the recommendations, guidelines and standardsMethods A scoping systematic literature review was undertaken in 2017-2018, which aligned with PRISMA guidelines and searched academic databases and grey literature published between 2007 and 2017.Results Eighty-nine documents included recommendations or outcomes on research methods for studies on chronic wound diagnosis, assessment, management and/or prevention; covering the areas of research design, sampling, randomisation and blinding, independent and outcome measures and interventions for research in chronic wounds.Common themes regarding research gaps and flaws were identified.Conclusion This review identified existing evidence, guidelines, recommendations and standards regarding the conduct of chronic wound research internationally.Recommendations include the need for standardised vocabulary, standardised checklists for wound research, development of core outcome datasets and an agreed and standardised set of economic parameters and methodology for cost-effectiveness.Establishment of a centralised national methodology service for wound research to assist with methodology design would be beneficial.
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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 | 0.386 | 0.540 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.052 | 0.040 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 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; the direct Gemma label and the distilled Codex classifier 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".