Case – Fungal urosepsis after ureteroscopy in a patient on new generation of anti-hyperglycemic medication
Bibliographic record
Abstract
Diabetes mellitus is a growing disease with extensive health risks.1 Gliflozin represents a new class of medications in the treatment armamentarium for diabetes. Medications in this class include dapagliflozin, canagliflozin, and empagliflozin. Their primary mechanism of action is blocking sodium-glucose transporter protein 2 (SGLT)-2, which inhibits the reabsorption of glucose in the kidney, and therefore lowers systemic blood sugar levels.2 SGLT-2 inhibitors are known to have higher urinary glucose levels as a byproduct of lowering blood glucose levels. Not surprisingly, patients on gliflozins have an increased incidence of urinary tract infections (UTIs), likely due to the associated glucosuria from the medication.3 Endoscopic urological interventions, particularly ones involving stone treatment, have a well-documented risk for causing UTIs. Prophylactic antibiotics are typically given in accordance with Canadian Urological Association guidelines and local resistance patterns to help reduce the risk of postoperative infections.4 Elevated urinary glucose levels can potentially increase the likelihood of post-endourological procedure infection. Urologists should, therefore, be aware of the higher risk of postoperative infections in patients on these novel agents and tailor their prophylactic antibiotics appropriately. Here, we report a case of a patient with type 2 diabetes mellitus who returned to hospital five days following an uneventful ureteroscopy with sepsis.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".