Histopathologic Examination and Reporting of Esophageal Carcinomas Following Preoperative Neoadjuvant Therapy
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
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.
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.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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