Dipsticks and diagnostic algorithms in urinary tract infection: development and validation, randomised trial, economic analysis, observational cohort and qualitative study
Bibliographic record
Abstract
OBJECTIVES: To estimate clinical and dipstick predictors of infection and develop and test clinical scores; to compare management using clinical and dipstick scores with commonly used alternative strategies; to estimate the cost-effectiveness of each strategy; and to understand the natural history of urinary tract infection (UTI) and women's concerns about its presentation and management. DESIGN: There were six studies: (1) validation development for diagnostic clinical and dipstick scores; (2) validation of the scores developed; (3) observation of the natural history of UTI; (4) randomised controlled trial (RCT) of scores developed in study 1; (5) economic analysis of the RCT; (6) qualitative study of patients in the RCT. SETTING: Primary care. PARTICIPANTS: Women aged 17-70 with suspected UTI. INTERVENTIONS: Patients were randomised to five management approaches: empirical antibiotics; empirical delayed antibiotics; target antibiotics based on a higher symptom score; target antibiotics based on dipstick results; or target antibiotics based on a positive mid-stream specimen of urine (MSU). MAIN OUTCOME MEASURES: Antibiotic use, use of MSUs, rates of reconsultation and duration, and severity of symptoms. RESULTS: (1) 62.5% of women had confirmed UTI. Only nitrite, leucocyte esterase and blood independently predicted diagnosis of UTI. A dipstick rule--based on having nitrite or both leucocytes and blood--was moderately sensitive (77%) and specific (70%) [positive predictive value (PPV) 81%, negative predictive value (NPV) 65%]. A clinical rule--based on having two of urine cloudiness, offensive smell, reported moderately severe dysuria, moderately severe nocturia--was less sensitive (65%) (specificity 69%, PPV 77%, NPV 54%). (2) 66% of women had confirmed UTI. The predictive values of nitrite, leucocyte esterase and blood were confirmed. The dipstick rule was moderately sensitive (75%) but less specific (66%) (PPV 81%, NPV 57%). (3) Symptoms rated as moderately bad or worse lasted 3.25 days on average for infections sensitive to antibiotics; resistant infections lasted 56% longer, infections not treated with antibiotics 62% longer and symptoms associated with urethral syndrome 33% longer. Symptom duration was shorter if the doctor was perceived to be positive about prognosis, and longer with frequent somatic symptoms, previous history of cystitis, urinary frequency and more severe symptoms at baseline. (4) 66% of the MSU group had laboratory-confirmed UTI. Women suffered 3.5 days of moderately bad symptoms if they took antibiotics immediately but 4.8 days if they delayed taking antibiotics for 48 hours. Taking bicarbonate or cranberry juice had no effect. (5) The MSU group was more costly over 1 month but not over 1 year. Cost-effectiveness acceptability curves showed that for a value per day of moderately bad symptoms of over 10 pounds, the dipstick strategy is most likely to be cost-effective. (6) Fear of spread to the kidneys, blood in the urine, and the impact of symptoms on vocational and leisure activities were important triggers for seeking help. When patients are asked to delay taking antibiotics the uncomfortable and worrying journey from 'person to patient' needs to be acknowledged and the rationale behind delaying the antibiotics made clear. CONCLUSIONS: To achieve good symptom control and reduce antibiotic use clinicians should either offer a 48-hour delayed antibiotic prescription to be used at the patient's discretion or target antibiotic treatment by dipsticks (positive nitrite or positive leucocytes and blood) with the offer of a delayed prescription if dipstick results are negative.
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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 | 0.136 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".