Prostate-Specific Antigen Test Utilization in a Major Canadian City
Bibliographic record
Abstract
OBJECTIVES: To review the utilization of prostate-specific antigen (PSA) testing in Winnipeg, a major Canadian city, and to compare PSA testing rates between Winnipeg and Calgary, another major Canadian city of comparable size. METHODS: PSA testing results were reviewed by year and age group. We focused our studies in years 2011 and 2016, for which census demographic data are available. RESULTS: In Winnipeg, the PSA testing rates (patients with one or two PSA tests divided by the male population) showed a declining trend over years from 2008 to 2017. For almost all age groups, PSA testing rates in 2016 decreased in comparison to those in 2011. For age older than 40 years, the relative percentage decreases were 14% to 20%.In 2011, Winnipeg PSA testing rates were consistently higher than those in Calgary for all age groups. For age older than 40 years, the relative percentage differences were 36% to 50%.In addition, 41% and 40% of patients in Winnipeg who underwent PSA testing were younger than 50 years or older than 69 years in 2011 and 2016, respectively. CONCLUSIONS: PSA testing utilization may be falling short of optimal rates. There is a need to reinforce the optimal use of clinical recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".