Guideline for optimization of colorectal cancer surgery and pathology
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is evidence of gaps in care for colorectal cancer surgery related to obtaining negative resection margins and lymph node assessment. Recommendations on the surgical and pathological management of curable colon and rectal cancer were developed. METHODS: A systematic review on colorectal resection margins and lymph nodes was conducted. This evidence, combined with evidence from existing guidelines and expert consensus, was used to develop recommendations. The draft guideline was reviewed by an expert panel and was externally reviewed by practitioners in Ontario, Canada. RESULTS: The search of the recent literature identified 107 articles pertinent to resection margins and lymph node assessment. The majority of the evidence was of poor quality. Of the 63 practitioners who reviewed the guideline, 97% agreed with the draft recommendations and 92% thought that the report should be approved as a practice guideline. CONCLUSIONS: Achieving optimized performance concerning margin status and lymph node assessment requires the coordinated efforts of surgeons and pathologists, as well as other medical professionals. Focus should be on ensuring that colorectal cancers are resected with negative (R0) margins and that an adequate number of lymph nodes are assessed to allow for accurate decision making relating to prognosis and adjuvant therapy.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.009 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".