Economic evaluation of compression therapies in the treatment of venous leg ulcers: a systematic review protocol
Bibliographic record
Abstract
Background Venous leg ulcers (VLU) are an important public health issue, impacting individuals' lives and representing a societal economic burden.Compression therapy is considered the best treatment for VLU, but there is no conclusive evidence whether the health benefits outweigh the costs.Objectives To identify and describe economic evaluations relating to compression therapy for the treatment of VLU and to evaluate the quality of these studies. MethodWe will conduct a systematic review of the MEDLINE, EMBASE, Cochrane Central, CINAHL, Scopus, Web of Science and LILACS databases and Google Scholar.We will include randomised controlled trials, pragmatic clinical trials, cohort studies, case-control studies and quasi-experimental studies which also describe an economic evaluation of compression therapy for VLU, published in English, Portuguese or Spanish.No time restriction will be applied.The screening and assessment will be done by two independent reviewers, supported by Covidence Software.ROB-2 and ROBINS-I tools will be used to assess risk of bias, and the CHEERS tool will support the assessment of the quality of economic evaluations. DiscussionThis systematic review will contribute to expand the knowledge about the topic.In addition, it may support health professionals' clinic decision making, assist managers regarding the allocation of resources, and improve the quality of life of individuals with VLU.
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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 | 0.070 | 0.079 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.016 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.076 | 0.008 |
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".