Bibliographic record
Abstract
Neoadjuvant chemoradiation (NACR) is now standard of care in stage II and III rectal cancer. The advent of this modality of treatment has impacted on the way the pathological evaluation of resection specimens that have been subjected to preoperative chemoradiation is conducted. The gross description, sectioning and microscopic examination have had to be adapted to accommodate the changes induced by NACR. Attempts at introducing a uniform approach to the gross triaging and reporting of these specimens have been met with muted response. There still exists much variation in approach. The purpose of this overview is to highlight some of the newer developments and issues around NACR-treated rectal cancers from a pathological point of view. The NACR-treated resection specimens should be handled in a consistent manner, at least within individual institutions, if not universally. There should be generous sampling with multiple sections taken as tumour is often sequestered deep in the bowel wall. Microscopic examination should be extra vigilant as residual cancer can be present as single cells or small clusters, often deep in the muscularis propria or serosa. Acellular pools of mucin or non-viable tumour cells in mucin within the bowel wall or lymph nodes are not regarded as positive and do not upstage the tumour. The issue of grading of regression has been the subject of much debate, and several approaches have been published. It is recommended that a system that has clinical meaning and use to oncologists be used. Lymph node counts will be reduced after NACR, but reasonable attempts to accrue 12 nodes should be made.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".