Polyhexanide Versus Metronidazole for Odor Management in Malignant (Fungating) Wounds
Bibliographic record
Abstract
PURPOSE: The aim of this study was to compare the effects of 0.2% polyhexamethylene biguanide (PHMB) to 0.8% metronidazole on malignant wound (MW) odor, health-related quality of life (HRQOL), and pain upon application. DESIGN: A double-blinded, randomized, clinical trial. SUBJECTS AND SETTING: Twenty-four patients with malodorous MWs hospitalized in a referral cancer center in Sao Paulo, Brazil, participated in the trial. METHODS: Participants were randomly allocated to treatment with 0.8% metronidazole solution (control group) or 0.2% PHMB (experimental group). Study outcomes were measured at baseline (day 0), 4 days, and 8 days. The primary end point was the odor that was measured in terms of its intensity, quality, and impact on participants during the study period. Health-related quality of life was measured with the Ferrans and Powers Quality of Life Index-Wounds Version (FPQLI-WV) on day 0 and on the day when odor was completely eliminated as per evaluation by the investigators. Pain intensity related to application of the control and experimental solutions was measured as a secondary outcome using a scale of 0 to 10. RESULTS: Twenty patients (83.3%) were classified as having "no wound odor" at 4 days, and 100% achieved no wound odor by day 8 (P < .001). Odor control in patients with MW significantly influenced their general HRQOL (P = .002). We found no difference in odor elimination, or HRQOL, when patients managed with PHMB were compared to those managed with metronidazole. There were no statistically significant differences over time in pain measurement between the 2 groups. CONCLUSIONS: Both PHMB and metronidazole significantly reduced odor in malodorous MWs within 4 days. Neither solution was found to be more effective than the other in the magnitude of odor reduction or its effect on condition-specific HRQOL.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".