What is your count? An observational study of lymph node counting in 2,028 colorectal cancer resections
Bibliographic record
Abstract
BACKGROUND: Lymph node status and lymph node count (LNC) are predictors of colorectal cancer outcome. Under-sampling of lymph nodes may lead to clinically relevant stage migration. METHODS: Colorectal cancer (CRC) cases with a synoptic report, accessioned 2012-2020 at a regional laboratory, were extracted and retrospectively studied. LNC, positive lymph node count (PLNC), tumour deposits present (TDpos), and 'y' (staging) prefix (YS) were retrieved and tabulated by pathologist using custom software. Statistical analyses were done with R. DATA AND RESULTS: The cohort had 2,543 CRC resections. Seventeen pathologists interpreted >50 cases (range: 56-356) each and collectively saw 2,074. After cases with unavailable data were purged, 2,028 cases remained with 43,996 lymph nodes, of which 2,637/43,996 were positive. 368 cases had a 'y' prefix, and 379 had TDpos. The 17 pathologists' median LNC/case was 19.0 (range: 14.0-24.0), and the mean PLNC per case was 1.4 (range: 1.0-2.0). Kruskal-Wallis rank sum tests showed there were differences in LNC (p<0.001) among pathologists; however, PLNC did not show this association (p = 0.2917). T-tests showed that mean LNC (p<0.001) and PLNC (p<0.035) differed between YS. 138 of 2,028 cases had less than the 12 LNC target. Logistic regression revealed a strong association between meeting the LNC target and pathologist (p<0.001) but TDpos was non-predictive (p = 0.4736). CONCLUSIONS: Positive lymph node call rate has a good consistency in the laboratory; however, lymph node count varies significantly between pathologists. Standardized counting criteria are needed to improve uniformity and could be aided by synoptic reporting data.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".