The predictive value of apoptosis protease‐activating factor 1 in rectal tumors treated with preoperative, high‐dose‐rate brachytherapy
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to assess the value of apoptosis protease-activating factor 1 (APAF-1) as a predictive marker of response in rectal tumors treated with preoperative, high-dose-rate endorectal brachytherapy. METHODS: Immunohistochemistry for APAF-1 was performed on 94 rectal tumor biopsy specimens from patients who were treated on a preoperative, high-dose-rate brachytherapy protocol. Tumors were considered positive when > 10% of tumor cells were immunoreactive. The association between APAF-1 expression and tumor response was made using the chi-square test. RESULTS: Forty-four tumors (43%) were positive for APAF-1. Thirty tumors had complete pathologic tumor regression after preoperative radiotherapy. Of these, 18 tumors were positive for APAF-1. A partial response occurred in 35 tumors. Eighteen tumors (51%) were positive for the protein. Only 8 of 29 nonresponsive tumors (28%) were immunoreactive for APAF-1. A significant association was found between complete tumor regression and positive APAF-1 status (P = 0.018). APAF-1 expression in partially responsive tumors was significantly greater than in nonresponsive tumors (P = 0.03). CONCLUSIONS: APAF-1 expression in pretreatment rectal tumor biopsy specimens may be useful as a predictive marker of response to preoperative radiotherapy in patients with rectal carcinoma.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".