The association between bacteria and outcome and the influence of sampling method, in people with a diabetic foot infection
Bibliographic record
Abstract
PURPOSE: Different bacteria lead to divers diabetic foot infections (DFIs), and some bacteria probably lead to higher amputation and mortality risks. We assessed mortality and amputation risk in relation to bacterial profiles in people DFI and investigated the role of sampling method. METHODS: We included people (> 18 years) with DFI in this retrospective study (2011-2020) at a Dutch tertiary care hospital. We retrieved cultures according to best sampling method: (1) bone biopsy; (2) ulcer bed biopsy; and (3) swab. We aggregated data into a composite determinant, consisting of unrepeated bacteria of one episode of infection, clustered into 5 profiles: (1) Streptococcus and Staphylococcus aureus; (2) coagulase-negative Staphylococcus, Cutibacterium, Corynebacterium and Enterococcus; (3) gram-negative; (4) Anaerobic; and (5) less common gram-positive bacteria. We calculated Hazard Ratio's (HR's) using time-dependent-Cox regression for the analyses and investigated effect modification by sampling method. RESULTS: We included 139 people, with 447 person-years follow-up and 459 episodes of infection. Sampling method modified the association between bacterial profiles and amputation for profile 2. HR's (95% CI's) for amputation for bacterial profiles 1-5: 0.7 (0.39-1.1); stratified analysis for profile 2: bone biopsy 0.84 (0.26-2.7), ulcer bed biopsy 0.89 (0.34-2.3), swab 5.9*(2.9-11.8); 1.3 (0.78-2.1); 1.6 (0.91-2.6); 1.6 (0.58-4.5). HR's (95% CI's) for mortality for bacterial profiles 1-5: 0.89 (0.49-1.6); 0.73 (0.38-1.4); 2.6*(1.4-4.8); 1.1(0.58-2.2); 0.80(0.19-3.3). CONCLUSIONS: In people with DFI, there was no association between bacterial profiles in ulcer bed and bone biopsies and amputation. Only in swab cultures, low-pathogenic bacteria (profile 2), were associated with a higher amputation risk. Infection with gram-negative bacteria was associated with a higher mortality risk. This study underlined the possible negative outcome of DFI treatment based on swabs cultures.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".