Collaborative Case Conferences in Rectal Cancer: Case Series in a Tertiary Care Centre
Bibliographic record
Abstract
BACKGROUND: In many hospitals, resource barriers preclude the use of preoperative multidisciplinary cancer conferences (mccs) for consecutive patients with cancer. Collaborative cancer conferences (cccs) are modified mccs that might overcome such barriers. METHODS: We established a ccc at an academic tertiary care centre to review preoperative plans for patients with rectal cancer. Attendees included only surgeons who perform colorectal cancer procedures and a radiologist with expertise in cross-sectional imaging. Individual reviews began with the primary surgeon presenting the case information and initial treatment recommendations. Cross-sectional images were then reviewed, the case was discussed, and consensus on ccc-treatment recommendations was achieved. Outcomes for the present study were changes in treatment recommendations defined as "major" (that is, redirection of patient to preoperative radiation from straight-to-surgery or uncertain plan, or redirection of the patient to straight-to-surgery from preoperative radiation or plan uncertain) or as "minor" (that is, referral to a multidisciplinary cancer clinic, request additional tests, change type of neoadjuvant therapy, change type of surgery). Chart reviews provided relevant patient, tumour, and treatment information. RESULTS: Between September 2011 and September 2012, 101 rectal cancer patients were discussed at a ccc. Of the 35 management plans (34.7%) that were changed as a result, 8 had major changes, and 27 had minor changes. Available patient and tumour factors did not predict for a change in treatment recommendation. CONCLUSIONS: Preoperative cccs at a tertiary-care centre changed treatment recommendations for one third of patients with rectal cancer. Given that no specific factor predicted for a treatment plan change, it is likely prudent that all rectal cancer patients undergo some form of collaborative 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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".