Prostate cancer screening: Canadian guidelines 2011
Bibliographic record
Abstract
Methods and data collectionA systematic literature search was conducted in the following electronic bibliographic databases: MEDLINE, including PreMedline (2004 to November 2010), EMBASE (2004 to Week 44, 2010) and the Cochrane Central Register of Controlled Trials (2010, 4th Quarter).This search was restricted to studies published in English.The search queries were based on a combination of exploded and non-exploded subject headings and free-text keywords.These terms included prostate cancer, prostatic neoplasms, prostate tumour, prostate-specific antigen (PSA), digital rectal examination (DRE), DRE, mass screening, screening test, early detection of cancer, cancer screening, screening, PSA, transrectal ultrasound (TRUS), TRUS, randomized, false-negative and false-positive; we used alternative word spellings and endings.The search strategy was modified for each database using database-specific thesaurus terms, syntax and search fields.We excluded case reports, editorials, news and letters.To identify additional relevant studies, we also examined bibliographies of the relevant articles and selected reviews.We compiled 1938 unique citations and, after removing the duplicates, 1036 citations were assessed for relevance.The screening process yielded 49 articles for a full-text review.
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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.029 | 0.036 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".