Bibliographic record
Abstract
There is a high incidence of urinary tract infection (UTI) in patients with neurogenic lower urinary tract function. This results in significant morbidity and health care utilization. Multiple well-established risk factors unique to a neurogenic bladder (NB) exist while others require ongoing investigation. It is important for care providers to have a good understanding of the different structural, physiological, immunological and catheter-related risk factors so that they may be modified when possible. Diagnosis remains complicated. Appropriate specimen collection is of paramount importance and a UTI cannot be diagnosed based on urinalysis or clinical presentation alone. A culture result with a bacterial concentration of ≥10(3) CFU/mL in combination with symptoms represents an acceptable definition for UTI diagnosis in NB patients. Cystoscopy, ultrasound and urodynamics should be utilized for the evaluation of recurrent infections in NB patients. An acute, symptomatic UTI should be treated with antibiotics for 5-14 days depending on the severity of the presentation. Antibiotic selection should be based on local and patient-based resistance patterns and the spectrum should be as narrow as possible if there are no concerns regarding urosepsis. Asymptomatic bacteriuria (AB) should not be treated because of rising resistance patterns and lack of clinical efficacy. The most important preventative measures include closed catheter drainage in patients with an indwelling catheter and the use of clean intermittent catheterization (CIC) over other methods of bladder management if possible. The use of hydrophilic or impregnated catheters is not recommended. Intravesical Botox, bacterial interference and sacral neuromodulation show significant promise for the prevention of UTIs in higher risk NB patients and future, multi-center, randomized controlled trials are required.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".