Patients with microscopic and gross hematuria: practice and referral patterns among primary care physicians in a universal health care system
Bibliographic record
Abstract
Background: Hematuria is one of the most common findings onurinalysis in patients encountered by primary care physicians. Inmany instances it can also be the first presentation of a serious urologicalproblem. As such, we sought to evaluate current practicesadopted by primary care physicians in the workup and screeningof hematuria.Methods: Questionnaires were mailed to all registered primary carephysicians across Quebec. Questions covered each physician’spersonal approach to men and postmenopausal women with painlessgross hematuria or with asymptomatic microscopic hematuria,as well as screening techniques, general knowledge with regardsto urine collection and sampling, and referral patterns.Results: Of the surveys mailed, 599 were returned. Annual routinescreening urinalysis on all adult male and female patients wasperformed by 47% of respondents, regardless of age or risk factors.Of all the respondents, 95% stated microscopic hematuria wasassociated with bladder cancer. However, in an older male withpainless gross hematuria, only 64% of respondents recommendedfurther evaluation by urology. On the other hand, in a postmenopausalwoman with 2 consecutive events of significant microscopichematuria, only 48.6% recommended referral to urology. Findingswere not associated with the gender of the respondent, experienceor geographic location of practice (urban vs. rural).Interpretation: There seems to be reluctance amongst primary carephysicians to refer patients with gross or significant microscopichematuria to urology for further investigation. A higher level ofsuspicion and further education should be implemented to detectserious conditions and to offer earlier intervention when possible.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".