Bibliographic record
Abstract
Despite the myriad of genetic and epigenetic alterations in human neoplasms that seem to demand specific molecular probes for their identification and practical application to diagnostic pathology, immunohistochemistry (IHC) remains a vital component of laboratory testing in the emerging molecular era. The development and proper application of sensitive and specific antibodies raised against cryptic proteins only expressed in quantity after gene translocation, translocation-specific chimeric fusion peptides, and gene products overexpressed because of gene amplification demonstrate that IHC is a legitimate surrogate for traditional cytogenetic and in situ hybridization-based identification of chromosomal abnormalities, if not a viable molecular technique in its own right. Similarly, the detection of mutational events, through the reliable demonstration of protein loss, the identification of proteins overexpressed because of activating mutations, the specific visualization of mutant gene products, and the localization of splice variant gene products emphasizes the potential value of IHC as a surrogate for mutational analyses of genes important to both diagnosis and prediction of therapeutic response. In the latter setting IHC also provides a means of approximating gene expression profiles in the molecular classification and risk stratification of human neoplasms. For time being, the application of appropriately targeted sensitive and specific antibodies provides a cost-effective screening modality, if not replacement, for selected molecular techniques, but IHC will lose its value if the development of companion tests for emerging novel biomarkers does not keep pace with molecular techniques, particularly as the costs and time constraints of genomic sequencing diminish over time.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".