Development of a simple multidisciplinary arthroplasty wound-assessment instrument: the SMArt Wound Tool
Bibliographic record
Abstract
Background: There are currently no validated instruments in the orthopedic literature for assessing the healing of acute surgical wounds. The creation of a simple wound-assessment tool would provide a standardized method of reporting wound outcomes. The objective of this study was to systematically develop a wound-assessment tool that can be used to assess the early healing of arthroplasty incisions. Methods: The databases MEDLINE, Embase, Cochrane Central Register of Controlled Trials, Cochrane reviews and CINAHL were searched. Articles that described objective assessment of acute incisional wounds were included. Items for the wound-assessment tool were then extracted from eligible studies based on the frequency of reporting. A multidisciplinary panel of wound experts compiled the items into an initial tool to assess key domains of wound healing. The items were reduced through several iterations of panel discussion. Results: Our search strategy yielded 3743 results, which were screened by title and abstract. Thirty-four studies were included in the systematic review for the development of the wound-assessment tool, and 10 domains were extracted based on frequency of reporting. After item reduction, the final version of the wound-assessment tool, the SMArt Wound Tool, contained 3 major domains: blistering, peri-incisional skin colour and exudate type. Conclusion: There is currently a need for a standardized tool to assess the healing of orthopedic surgical incisions. The SMArt Wound Tool provides a simple, objective method of assessing arthroplasty incisions for the presence of early complications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".