The use of transcutaneous oximetry to predict complications of chronic wound healing: A systematic review and meta‐analysis
Bibliographic record
Abstract
Transcutaneous oximetry (P(tc) O(2) ) has been proposed as a method to predict nonhealing of chronic wounds and the occurrence of healing complications. However, the validity of this method as an independent predictor and an optimal threshold value for this tool are poorly defined. We undertook a systematic review and meta-analysis to address these questions, searching five major medical databases, relevant review articles, and reference lists. We included all studies that used multivariable analysis to evaluate P(tc) O(2) for its ability to predict chronic wound healing complications. Article selection and data abstraction were conducted independently and in duplicate. Four studies, enrolling 901 patients with 910 lower extremity chronic wounds, met our inclusion criteria. These studies showed that a peri-wound P(tc) O(2) level below a cutoff of 20 mmHg or 30 mmHg was an independent predictor of chronic wound healing complications (odds ratio 3.21, 95% confidence interval 1.07-9.69, I(2) = 77%). A threshold value of 20 mmHg was used most frequently in the included studies and showed the strongest association. This review was limited by the small number of studies, a possible reporting bias, and heterogeneity. These results suggest that P(tc) O(2) measurements have independent prognostic value in the assessment of chronic wounds, but further research is required to confirm these findings and define an optimal threshold value.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".