Standardization of Positive Controls in Diagnostic Immunohistochemistry
Bibliographic record
Abstract
Diagnostic immunohistochemistry (dIHC) has been practiced for several decades, with an ongoing expansion of applications for diagnostic use, and more recently for detection of prognostic and predictive biomarkers. However, standardization of practice has yet to be achieved, despite significant advances in methodology. An Ad Hoc Expert Committee was formed to address the standardization of controls, which is a missing link in demonstrating and assuring standardization of the various components of dIHC. This committee has also developed a concept of immunohistochemistry critical assay performance controls that are intended to facilitate methodology transfer and harmonization in dIHC. Furthermore, the committee has clarified definitions of IHC assay sensitivity and specificity, with special emphasis on how these definitions apply to positive controls. Recommendations for “best laboratory practice” regarding positive controls for dIHC are specified. The first set of immunohistochemistry critical assay performance controls for several frequently used IHC stains or tests is also developed and presented.
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 | 0.087 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".