A protocol for a scoping review to identify methods used in clinical practice to assess wound odour
Bibliographic record
Abstract
Objective: The objective of this scoping review is to map, from wound assessment tools and other literature, the current methods used to assess wound odour in order to answer the following question: Which methods of assessment, validated or otherwise, are currently used in wound assessment tools to assess wound odour? Introduction: Wound assessment includes not only details of the condition of the wound bed but also evaluation of symptoms associated with the wound including that of odour. Odour is cited by clinicians, patients and carers as one of the most distressing wound symptoms. However, there is no consensus on a preferred method to assess odour thus negatively impacting the internal and external validity of many clinical trials and minimising the ability to perform meta-analysis. Eligibility criteria: Any wound assessment tool or framework that includes assessment of wound odour in any wound aetiology and in any care setting. Any systematic or scoping review that includes assessment of wound odour in any wound aetiology and in any care setting. No limits on date of publication or language will be applied. Methods: We will employ the Preferred Reporting Items for Systematic Review and Meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines for this scoping review and base its structure on the framework proposed by Arksey and O’Malley. Results: A narrative format will summarise extracted data and provide an overview of tools used to assess wound odour. A PRISMA diagram will outline the results of the search strategy. The identified tools will be summarised in table format and stratified according to methods used. Conclusion: The result of this scoping review will be a list of methods used to assess odour in wounds and will be used to inform a subsequent Delphi study to gain consensus on the preferred method to assess wound odour.
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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 | 0.177 | 0.268 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.284 | 0.084 |
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".