Standardization of Negative Controls in Diagnostic Immunohistochemistry
Bibliographic record
Abstract
Standardization of controls, both positive and negative controls, is needed for diagnostic immunohistochemistry (dIHC). The use of IHC-negative controls, irrespective of type, although well established, is not standardized. As such, the relevance and applicability of negative controls continues to challenge both pathologists and laboratory budgets. Despite the clear theoretical notion that appropriate controls serve to demonstrate the sensitivity and specificity of the dIHC test, it remains unclear which types of positive and negative controls are applicable and/or useful in day-to-day clinical practice. There is a perceived need to provide "best practice recommendations" for the use of negative controls. This perception is driven not only by logistics and cost issues, but also by increased pressure for accurate IHC testing, especially when IHC is performed for predictive markers, the number of which is rising as personalized medicine continues to develop. Herein, an international ad hoc expert panel reviews classification of negative controls relevant to clinical practice, proposes standard terminology for negative controls, considers the total evidence of IHC specificity that is available to pathologists, and develops a set of recommendations for the use of negative controls in dIHC based on "fit-for-use" principles.
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 | 0.153 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".