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Record W4286560699 · doi:10.1007/s15010-022-01884-x

The association between bacteria and outcome and the influence of sampling method, in people with a diabetic foot infection

2022· article· en· W4286560699 on OpenAlexaff
Meryl Cinzía Tila Tamara Gramberg, Shaya Krishnaa Normadevi Mahadew, Birgit I. Lissenberg‐Witte, Marielle Petra Bleijenberg, Jara R. de la Court, Jarne M. van Hattem, Louise Willy Elizabeth Sabelis, Rimke Sabine Lagrand, Vincent de Groot, Martin den Heijer, Edgar J.G. Peters

Bibliographic record

VenueInfection · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsInstitute of Infection and Immunity
FundersVrije Universiteit AmsterdamAmsterdam University Medical Centers
KeywordsMedicineHazard ratioInternal medicineAmputationEnterococcusProportional hazards modelMicrobiologyDiabetic footStaphylococcus aureusStaphylococcusRetrospective cohort studyDiabetic foot ulcerBacteriaSurgeryGastroenterologyBiologyDiabetes mellitusConfidence intervalAntibiotics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.307
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2022
Admission routes1
Has abstractyes

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