Utility of Gram Stain for the Microbiological Analysis of Burn Wound Surfaces
Bibliographic record
Abstract
Abstract Context. —Surface swab cultures have attracted attention as a potential alternative to biopsy histology or quantitative culture methods for microbiological burn wound monitoring. To our knowledge, the utility of adding a Gram-stained slide in this context has not been evaluated previously. Objective. —To determine the degree of correlation of Gram stain with culture for the microbiological analysis of burn wound surfaces. Design. —Prospective laboratory analysis. Setting. —Urban health region/centralized diagnostic microbiology laboratory. Patients. —Burn patients hospitalized in any Calgary Health Region burn center from November 2000 to September 2001. Intervention. —Gram stain plus culture of burn wound surface swab specimens obtained during routine dressing changes or based on clinical signs of infection. Main Outcome Measures. —Degree of correlation (complete, high, partial, none), including weighted κ statistic (κ w ), of Gram stain with culture based on quantitative microscopy and degree of culture growth. Results. —A total of 375 specimens from 50 burn patients were evaluated. Of these, 239 were negative by culture and Gram stain, 7 were positive by Gram stain only, 89 were positive by culture only, and 40 were positive by both methods. The degree of complete, high, partial, and no correlation of Gram stain with culture was 70.9% (266/375), 1.1% (4/375), 2.4% (9/375), and 25.6% (96/375), respectively. The degree of correlation for all 375 specimens, as expressed by the weighted κ statistic, was found to be fair (κ w = 0.32). Conclusion. —The Gram stain is not suitable for the microbiological analysis of burn wound surfaces.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".