Development of a synoptic MRI report for primary rectal cancer
Bibliographic record
Abstract
BACKGROUND: Although magnetic resonance imaging (MRI) is an important imaging modality for pre-operative staging and surgical planning of rectal cancer, to date there has been little investigation on the completeness and overall quality of MRI reports. This is important because optimal patient care depends on the quality of the MRI report and clear communication of these reports to treating physicians. Previous work has shown that the use of synoptic pathology reports improves the quality of pathology reports and communication between physicians. METHODS: The aims of this project are to develop a synoptic MRI report for rectal cancer and determine the enablers and barriers toward the implementation of a synoptic MRI report for rectal cancer in the clinical setting. A three-step Delphi process with an expert panel will extract the key criteria for the MRI report to guide pre-operative chemoradiation and surgical planning following a review of the literature, and a synoptic template will be developed. Furthermore, standardized qualitative research methods will be used to conduct interviews with radiologists to determine the enablers and barriers to the implementation and sustainability of the synoptic MRI report in the clinic setting. CONCLUSION: Synoptic MRI reports for rectal cancer are currently not used in North America and may improve the overall quality of MRI report and communication between physicians. This may, in turn, lead to improved patient care and outcomes for rectal cancer patients.
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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 | 0.066 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".