Canadian Association of Radiologists Cancer Diagnostic Imaging Referral Guideline
Bibliographic record
Abstract
The Canadian Association of Radiologists (CAR) Cancer Expert Panel is made up of physicians from the disciplines of radiology, medical oncology, surgical oncology, radiation oncology, family medicine/general practitioner oncology, a patient advisor, and an epidemiologist/guideline methodologist. The Expert Panel developed a list of 29 clinical/diagnostic scenarios, of which 16 pointed to other CAR guidelines. A rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for one or more of the remaining 13 scenarios. Recommendations from 21 guidelines and contextualization criteria in the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) for guidelines framework were used to develop the recommendation for these scenarios. During recommendation formulation, one additional scenario was mapped to an existing CAR guideline scenario, leaving 12 scenarios with new recommendations. The guideline focuses on cancer diagnosis and does not cover cancer staging, follow-up, and surveillance. This guideline presents the methods of development and the referral recommendations for suspected pancreatic cancer, suspected liver cancer, incidental liver mass, incidental colon mass or suspected colon cancer, suspected anal cancer, suspected penile cancer, suspected cervical cancer, suspected endometrial/uterine cancer, suspected vulvar cancer, suspected vaginal cancer, suspected haematologic malignancies, and suspected skin cancer. The guideline also points to other CAR guidelines for suspected neck, thyroid, brain, lung, intracardiac/pericardial, esophageal/gastric, renal, adrenal, bladder, testicular, prostate and ovarian cancers, suspected soft tissue mass or tumour, suspected bone tumour, suspected bone tumour --myeloma, suspected spine tumours, and incidental lung cancer.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".