Bibliographic record
Abstract
Breast cancer, the most prevalent female carcinoma, is characterized by the expression of steroid nuclear receptors in a subset of cases. The most important nuclear receptor with prognostic and therapeutic implications is the Estrogen Receptor (ER), which is expressed in about three out of four breast cancers. The Progesterone Receptor (PR) and the Androgen Receptor (AR) are also commonly expressed. Moreover, non-steroid nuclear receptors, including the vitamin D receptor (VDR) and the thyroid receptors (TRs), are also present in breast cancers and have pathophysiologic implications. Circulating thyroid hormones may influence breast cancer risk and breast cancer cell survival, through ligating their canonical receptors TRα and TRβ but also through additional membrane receptors that are expressed in breast cancer. The expression of TR subtypes and their respective isotypes have diverse effects in breast cancers through co-operation with ER and influence on other cancer-associated pathways. Other components of the TSH/thyroid hormone axis, such as TSH and selenoiodinase enzymes, have putative effects in breast cancer pathophysiology. This paper reviews the pathophysiologic and prognostic implications of the thyroid axis in breast cancer and provides a brief therapeutic perspective.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".