Lymph node counts, rates of positive lymph nodes, and patient survival for colon cancer surgery in Ontario, Canada: A population-based study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This study assessed lymph node counts, lymph node status (positive or negative), and survival among patients undergoing colon cancer surgery in Ontario, Canada. METHODS: We obtained data from the Ontario Cancer Registry on 960 patients who underwent a major colon cancer resection in years 1991-1993. Patients and hospitals were ranked by lymph node count to correlate lymph node counts and lymph node status. For node-negative patients we assessed the influence of patient, hospital, and tumor factors on lymph node counts and survival. RESULTS: The rate of node-positive patients was similar among the lymph node count groups. For example, the odds ratio of a patient being node positive if the lymph node count was 10-36 versus 1-3 was 1.0 (CI 0.6-1.6, P = 0.42). Among node-negative patients, survival was improved for patients with a high (10-36) versus low (1-3) lymph node count (HR 0.6, CI 0.4-1.0, P = 0.03). No patient, hospital, or tumor factors predicted both a higher lymph node count and improved survival. CONCLUSIONS: In this population-based study of patients undergoing colon cancer surgery, higher lymph node counts did not correlate with increased rates of node-positive status.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it