Histopathologic Examination and Reporting of Esophageal Carcinomas Following Preoperative Neoadjuvant Therapy
Bibliographic record
Abstract
Neoadjuvant chemoradiotherapy is being increasingly offered to patients with invasive esophageal carcinoma in an effort to downstage the tumor and consequently increase the rate of curative resection. A substantial amount of data has suggested that pathologic tumor regression following neoadjuvant therapy is an important predictor of local recurrence and long-term survival in esophageal cancer. Therefore, it is important that these posttreatment resection specimens are handled in a standardized manner and a reproducible method of tumor regression grading is used. Pathologic examination of such specimens is not straightforward, and, in fact, it presents a particular challenge to pathologists, especially when a good response to neoadjuvant therapy has been achieved and little or no residual tumor remains. We provide some guidelines for handling and reporting such specimens and outline the commonly used tumor regression grading systems for posttreatment esophagectomy specimens.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".