Correlation of thyroid hormone, retinoid X, peroxisome proliferator-activated, vitamin D and oestrogen/progesterone receptors in breast carcinoma
Bibliographic record
Abstract
Non-steroidal nuclear receptors play a major role in breast cancer development. A correlation among, and possible prognostic function of, the members of the nuclear receptor superfamily has been discussed controversially over the years. Hence, we conducted a quantification of the different expression levels of the thyroid receptor (TR), retinoid X receptor (RXR), peroxisome proliferator-activated receptor (PPAR) and vitamin D receptor (VDR) in malignant breast tumour tissue samples. Patients diagnosed and treated for breast cancer between 1990 and 2000 were included. Receptor expression was detected by immunohistochemical staining. Correlation analyses for the expression of the receptors were performed for the clinical and histopathological data. The paraffin-embedded tissue from 82 breast cancer patients was available. The different steroid receptors showed varying results when correlated with known histopathological markers. TRα2 demonstrated the most significant correlations with steroid hormone receptors. Significant correlations between the major isoforms of TR, and between RXR, PPAR and VDR, were demonstrated in the patient sample. The immunohistochemical association of these receptors may provide the first proof of an interaction on the molecular level. This assumption awaits confirmation in studies with larger cohorts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".