Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this article is to review recent publications relevant to understanding the interaction of urinary tract infection and diabetes mellitus, including epidemiology, pathogenesis, microbiology, and treatment. RECENT FINDINGS: The largest number of identified reports described aspects of epidemiology, including defining the incidence and outcomes of urinary infection in patients with diabetes. In several reports, mortality and risk of hospitalization for urinary infection were not increased with diabetes, although length of hospitalization may be prolonged. Other reports quantify the increased incidence of cystitis or pyelonephritis in persons with diabetes, but remain subject to potential biases which could overestimate the occurrence in diabetic relative to non-diabetic populations. Several reports suggest that resistant bacteria are more frequently isolated from diabetic outpatients with urinary infection, but it is not clear how this is directly attributable to diabetes. There are no recent clinical trials which enhance our understanding of optimal treatment of symptomatic urinary infection, although several review articles acknowledge the appropriateness of the non-treatment of asymptomatic bacteriuria in diabetic women. SUMMARY: Recent reports exploring diabetes and urinary tract infection provide some insights, particularly for risks of infection and outcomes, but there are no recent large advances in the knowledge base. Questions related to incidence, optimal treatment, and role of metabolic control still need to be addressed to expand the knowledge base and enhance management of this common problem.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".