A comparative analysis of rapid urine tests for the diagnosis of upper urinary tract malignancy.
Bibliographic record
Abstract
OBJECTIVES: To compare the effectiveness of two rapid urine tests fibrinogen/fibrin degradation products FDP (Aura Tek FDP, PerImmune Inc., Rockville, Maryland, USA) and bladder tumor antigen BTA (Bard BTA, Bard Canada Inc., Mississauga, Ontario, Canada) to urinary cytology in establishing the diagnosis of transitional cell carcinoma (TCC) in patients with suspected upper tract malignancy. MATERIALS AND METHODS: In a prospective study, urine samples were collected from 29 patients with abnormalities of the upper tracts highly suspicious for malignancy. Sensitivity and specificity of the BTA and FDP tests were determined and compared to those of cytology. All persons interpreting the individual tests were blinded to the other test results. RESULTS: Of the 29 patients evaluated, 14 were found to have upper tract TCC. The overall sensitivity of FDP, BTA and urinary cytology was found to be 100%, 50% and 29%, respectively. The accuracy of the FDP test was 83% as compared to 62% for BTA and 59% for cytology. CONCLUSION: Urinary cytology lacks sensitivity in the diagnosis of upper tract TCC. This preliminary study suggests that the point-of-care test, FDP, has better test characteristics than urinary cytology. Further evaluation of these tests is warranted for their potential to improve and facilitate the diagnosis of upper tract tumors.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".