Clinical Information Available to Oncologists in Surgically Treated Rectal Cancer: Room to Improve
Bibliographic record
Abstract
INTRODUCTION: In rectal cancer, decisions about the use of adjuvant and neoadjuvant treatment rely on clinical information from a variety of sources. Currently, the quality and accuracy of the aggregate of this clinical information is unclear. The objectives of the present study were to evaluate the completeness and quality of clinical information available to oncologists managing rectal cancer. METHODS: All patients diagnosed with rectal cancer in Nova Scotia between 2001 and 2005 were identified through the provincial cancer registry. The registry was linked to other administrative databases to obtain demographic, diagnostic, and treatment data. Patients undergoing radiation oncology consultation were identified, and a standardized review of the cancer centre chart was performed on a random sample, stratified by year. RESULTS: For the 222 patients reviewed, the relevant endoscopy report was present in 113 cases (51%). The level of the tumour was documented in 75% of those reports, and colonoscopy completeness, in 81%. The relevant operative report was available in 192 cases (87%). Tumour level was described in 59% of those reports, and local extension, in 73%. Elements of total mesorectal excision were partially described in 97%. In pathology reports (10% of which were synoptic), we observed significant variability in the presence of important elements. Reporting of those elements was significantly better in the synoptic pathology reports. CONCLUSIONS: Clinical information related to adjuvant and neoadjuvant therapy decision-making in rectal cancer is often not available or incomplete. A synoptic reporting system in endoscopy, surgery, and pathology could potentially be a beneficial tool in rectal cancer care.
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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 | 0.106 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".