A-215 Slightly cloudy urine: a benign finding or a call for microscopy?
Bibliographic record
Abstract
Abstract Background Urinalysis testing for community patients in Alberta, Canada is mainly conducted at two central laboratories using automated analyzers. These analyzers employ reflex rules to trigger automated microscopy when certain criteria are met. Although microscopy is automated, many images still require manual review before confirming results. Therefore, it is essential to establish appropriate reflex rules that maximize the detection of pathological elements while minimizing unnecessary reflex microscopy and workload. Currently, our laboratories reflex urine samples to microscopy when there is abnormal color, non-clear clarity, or positive results for blood, protein, nitrites, or leukocytes. Historically, samples with negative chemical urinalysis findings and slight cloudiness did not trigger reflex microscopy. Introducing this criterion as part of standardization initiatives has increased staff workload by requiring additional image reviews from automated microscopy instruments. This study aimed to assess the frequency of pathological elements detected in samples reflexed based solely on slight cloudiness and the impact of introducing this rule on workload. Methods Urinalysis results from July 2023 to June 2024 were extracted from the laboratory information system (LIS) for both community laboratories. Data analysis was conducted using RStudio (version 4.3.0) to determine the percentage of slightly cloudy samples that had clinically significant microscopy findings, defined as any abnormal findings flagged in the electronic medical record. To assess the added value of reflexing slightly cloudy samples, microscopy findings were compared among three groups: clear, slightly cloudy, and cloudy samples. Microscopy results from 100 clear samples with no flagging criteria were obtained by manually reflexing to automated microscopy. Samples that reflexed to microscopy due to additional flagging criteria beyond the clarity criteria were excluded from the analysis. Results A total of 1,286,865 urinalysis results from Beckman iChem® VELOCITY and iQ® 200 SPRINT analyzers were retrieved from the LIS, with 99,212 samples (7.7%) reflexed to microscopy solely due to a slightly cloudy clarity result. This added approximately 270 microscopy reviews per day, potentially requiring staff review. Of these, 43.4% showed abnormal microscopy findings, compared to 53.1% of cloudy samples and 7.0% of manually reflexed clear samples. In the slightly cloudy group, abnormal findings included red blood cells (>2/HPF) in 8.9% of samples, white blood cells (>5/HPF) in 1.2%, squamous/transitional epithelial cells (>5/HPF) in 13.5%, bacteria (>20/HPF) in 4.0%, yeast in 0.8%, hyaline casts (>2/HPF) in 3.5%, calcium oxalate in 20.8%, and uric acid in 0.6%. Conclusion Reflexing urine samples to microscopy based solely on slightly cloudy clarity is beneficial for detecting abnormal elements. Of these samples, 43.4% had abnormal results which is significantly higher than the 7.0% found in clear samples and only slightly lower than the 53.1% observed in cloudy samples. This demonstrates a significant association between slightly cloudy urine and pathological elements, reinforcing the need for its inclusion in microscopy reflex criteria. Including slightly cloudy samples in reflex criteria improves the detection of abnormal findings, making the added workload worthwhile. This approach enhances the identification of clinically significant elements, supporting better patient care and more effective urinalysis screening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".