Evaluating the impact of lymph node resampling on colorectal cancer nodal stage
Bibliographic record
Abstract
AIMS: Nodal staging in colorectal cancer (CRC) informs prognosis and guides adjuvant treatment decisions. A standard minimum of 12 lymph nodes is widely used, with additional sampling being performed as required. However, there are few data on how lymph node resampling in this context has an impact on nodal stage. The aims of this study were to evaluate the effectiveness of resampling in detecting metastases and tumour deposits, and the impact on stage. METHODS AND RESULTS: A retrospective cohort analysis was performed on CRC resections that underwent resampling because of an initial yield of <12 lymph nodes, from 2008 to 2018. Data relating to patient demographics, specimen, malignancy and prosection were collected. Slides were reviewed to quantify nodal metastases and tumour deposits before and after resampling. Among ≥pN1 cases, logistic regression analysis was performed to evaluate factors that predicted the finding of additional metastases and tumour deposits. The cohort comprised 395 cases: resampling identified nodal metastases and/or tumour deposits in 30 (7.6%) cases; nodal upstaging occurred in 20 (5.1%) cases; and eight (2.0%) cases changed from pN0 to ≥pN1. No factors predicted resampling of positive lymph nodes or tumour deposits, and pN upstaging occurred across a variety of cases. A subgroup analysis was performed to assess the impact of resampling on high-risk features in stage II cases (n = 117). There were 33 (8.5%) patients who no longer had any high-risk features after resampling. CONCLUSIONS: Lymph node resampling has an impact on nodal staging and possible treatment decisions in a considerable proportion of patients, and is recommended in all cases with <12 lymph nodes.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".