Immunocytology in the assessment of patients with painless gross haematuria
Bibliographic record
Abstract
OBJECTIVE: To evaluate, in a prospective study, the role of immunocytology in assessing patients with gross haematuria. Due to the high prevalence of urothelial cancer in this population, a thorough assessment is mandatory to identify all patients with tumours. PATIENTS AND METHODS: We used Ucyt (DiagnoCure Inc., Quebec, Canada), a commercially available immunocytological assay based on the microscopic detection of tumour-associated antigens on the membrane of urothelial cells by immunofluorescence. Between October 2000 and March 2007, 61 consecutive patients with a first episode of painless gross haematuria, but no previous transitional cell carcinoma, were included. Urine samples were obtained from all patients and examined cytologically and immunocytologically. RESULTS: Clinically (by physical examination, laboratory tests, endoscopy and imaging) there was bladder cancer in 17 patients (28%); further diagnoses were benign prostatic enlargement (20, 33%), urinary tract infection (seven, 12%), urolithiasis (two, 3%), and 'further conditions' (seven, 12%). In 10 patients (16%) the reasons for haematuria were not disclosed. Of the 61 samples, 59 (97%) were assessable by cytology and immunocytology. For cystoscopy, immunocytology and conventional urine cytology the sensitivity was 76%, 88% and 47%, and the specificity 100%, 77% and 95%, respectively. Two bladder tumours were not detected by cystoscopy and immunocytology (one each), and two upper urinary tract tumours were diagnosed by imaging and immunocytology. CONCLUSIONS: The combination of cystoscopy and immunocytology gave 100% sensitivity, while combining cystoscopy and cytology only marginally improved the sensitivity of cystoscopy alone. As sensitivity appears to be of key relevance in assessing patients with gross haematuria, we suggest adding immunocytology to the diagnostic protocol in this situation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".