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Record W3005871454 · doi:10.2337/db2020-01

Diagnosis and Management of Diabetic Foot Infections

2020· article· en· W3005871454 on OpenAlex
Andrew J.M. Boulton, David G. Armstrong, Matthew J. Hardman, Matthew Malone, John M. Embil, Christopher E. Attinger, Benjamin A. Lipsky, Javier Aragón‐Sánchez, Ho Kwong, Gregory S. Schultz, Robert S. Kirsner

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueADA Clinical Compendia · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDiabetic footOsteomyelitisDebridement (dental)MedicineCompendiumAntibioticsSurgical debridementIntensive care medicineSurgeryDiabetes mellitusBiologyMicrobiologyGeography

Abstract

fetched live from OpenAlex

The first sections herein cover the impact of infection on healing of experimental wounds (p. 2), the importance of biofilms (p. 4), and a general overview of the microbiology of DFIs (p. 6). Although debridement of DFUs was covered in the first compendium, we deemed it important enough to include here as well, given its pivotal role in the management of DFIs. Subsequent sections cover the management of infected DFUs (p. 9) and discussions of antibiotics versus surgery for osteomyelitis (p. 12) and the OVIVA trial (p. 13). There is little doubt that the OVIVA trial will challenge the current management of osteomyelitis, in which IV antibiotics are still commonly used. The remaining sections cover potential topical treatments for DFIs (p. 15) and the role of modern technology in infection control (p. 17).

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.086
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

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

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 head0.074
GPT teacher head0.373
Teacher spread0.299 · 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