Recomendaciones para la determinación de HER2 en cáncer de mama. Consenso Nacional de la Sociedad Española de Anatomía Patológica ( SEAP ) y de la Sociedad Española de Oncología Médica ( SEOM )
Bibliographic record
Abstract
Breast cancers with HER2 alterations are critical to identify because such tumors require unique treatment, including the use of targeted therapies. HER2 alterations at the DNA (amplification) and protein (overexpression) level usually occur in concert, and both in situ hybridization and immunohistochemistry can be accurate methods to assess these alterations. However, recent studies including those conducted by the Association for Quality Assessment of the Spanish Society of Pathology and the experience of several national reference centres for HER2 testing have suggested that serious reproducibility issues exist with both techniques. To address this, a joint committee of both the Spanish Society of Pathology and the Spanish Society of Medical Oncology has met to review guidelines for HER2 testing. Consensus recommendation are based not only on panellist’s experience but also in those consensus guidelines previously reported in several countries, such as United Stated, United Kingdom and Canada. These guidelines include minimal requirements that Pathology Department must meet in order to guarantee appropriate HER2 testing in breast cancer. Pathology laboratories that do not meet these standards must put effort to reach them and, in the meantime, send clinical cases to reference centres.
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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 | 0.045 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".