Intratumoral Immune Responses Can Distinguish New Primary and True Recurrence Types of Ipsilateral Breast Tumor Recurrences (IBTR)
Bibliographic record
Abstract
Ipsilateral breast tumor recurrence (IBTR) is an increasingly common clinical challenge. IBTRs include True Recurrences (TR; persistent disease) and New Primaries (NP; de novo tumors), but discrimination between these is difficult. We assessed tumor infiltrating leukocytes (TIL) as biomarkers for distinguishing these types of IBTR using primary tumors and matched IBTRs from 24 breast cancer patients, half of which were identified as putative TRs and half as NPs using a previously reported clinical algorithm. Intratumoral lymphocyte populations (CD3, CD8, CD4, CD25, FOXP3, TIA1, CD20) and macrophages (CD68) were quantified by immunohistochemistry in each tumor. Compared to matched primaries, TRs showed significant trends towards increased CD3(+) and CD8(+) TIL, while these populations were often diminished in NPs. Comparison of IBTRs showed that TRs had significantly higher levels of CD3(+) (P = 0.0136), CD8(+) (P = 0.0092), and CD25(+) (P = 0.0159) TIL than NPs. We conclude that TIL may be a novel diagnostic biomarker to distinguish NP from TR IBTRs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".