Diagnosis and Treatment of Diabetic Foot Infections
Bibliographic record
Abstract
Benjamin A. Lipsky, Anthony R. Berendt, H. Gunner Deery, John M. Embil, Warren S. Joseph, Adolf W. Karchmer, Jack L. LeFrock, Daniel P. Lew, Jon T. Mader, Carl Norden, and James S. Tan Medical Service, Veterans Affairs Puget Sound Health Care System, and Division of General Internal Medicine, Department of Medicine, University of Washington School of Medicine, Seattle, Washington; Bone Infection Unit, Nuffield Orthopaedic Centre, Oxford, United Kingdom; Northern Michigan Infectious Diseases, Petoskey, Michigan; Section of Infectious Diseases, Department of Medicine, University of Manitoba, Winnipeg, Manitoba; Section of Podiatry, Department of Primary Care, Veterans Affairs Medical Center, Coatesville, Pennsylvania; Division of Infectious Diseases, Department of Medicine, Harvard Medical School, and Beth Israel Deaconess Medical Center, Boston, Massachusetts; Dimensional Dosing Systems, Sarasota, Florida; Department of Medicine, Service of Infectious Diseases, University of Geneva Hospitals, Geneva, Switzerland; Department of Internal Medicine, The Marine Biomedical Institute, and Department of Orthopaedics and Rehabilitation, University of Texas Medical Branch, Galveston, Texas; Department of Medicine, New Jersey School of Medicine and Dentistry, and Cooper Hospital, Camden, New Jersey; and Department of Internal Medicine, Summa Health System, and Northeastern Ohio Universities College of Medicine, Akron, Ohio
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".