Linear regression is a reproducible model of tumor response to pre-operative endorectal brachytherapy
Bibliographic record
Abstract
3772 Background: The aim of this study was to evaluate linear regression using p21, bcl-2 and p53 immunohistochemistry as a reproducible tumor response model for patients undergoing pre-operative endorectal brachytherapy. Methods: Immunohistochemistry for p21, bcl-2 and p53 was performed on pre-treatment tumor biopsies from 51 patients with rectal adenocarcinoma. Patients underwent pre-operative endorectal brachytherapy (26 Gy in 4 fractions) followed by surgery 4–8 weeks after radiation. Tumors were classified as completely, partially or non-responsive to therapy by pathologic evaluation of the tumor post-operatively. Fifteen patients had complete tumor response (ypT0), 20 had partial response (microfoci of residual carcinoma) and 16 had no response (no histologic evidence of treatment-related tumor necrosis). Tumor cell staining was quantitatively assessed by at least two independent investigators. Each tumor biopsy was associated with 3 percentages X, Y and Z representing the immunoreactivity of p21, bcl-2 and p53 respectively. The coordinates X, Y and Z for biopsies of each response group were represented graphically and linear regression was performed. Tumor cell staining for the same proteins and 51 patients was assessed by a third independent investigator blinded to the previous scores 6–8 months later. Linear regression was performed with the new tumor coordinates for each response group. Results: The intraobserver variability for the two sets of scores is approximately 14%. The linear regression planes for both scores are similar and display significant differences between the tumor response groups. Graphical representation of the planes shows the most pronounced divergence to be between the completely and non-responsive groups as they appear as “mirror images” of each other. The linear regression model demonstrates the different relationships between p21, bcl-2 and p53 in the three tumor response groups. Conclusion: The quantitative evaluation of immunostaining and the subsequent representation of response groups by linear regression appear to be a reproducible model of rectal tumor response to pre-operative brachytherapy. No significant financial relationships to disclose.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".