Diagnostic Immunohistochemistry: What Can Go Wrong and How to Prevent It
Bibliographic record
Abstract
CONTEXT: -There are a number of critical factors that can lead to incorrect results if the diagnostic pathologist performing immunohistochemistry is unaware of, or not vigilant about, their influence. OBJECTIVE: -To highlight 3 arenas in which errors may be introduced. DATA SOURCES: -For choosing the correct primary antibody, selection of the most appropriate antibodies for a given clinical application can be aided by obtaining information from the vendor; however, this can yield incomplete information. There are a number of online databases that have comparisons of antibodies from different vendors, particularly with respect to their use and properties. Reading the published literature can assist in this process, particularly with respect to determining antibody sensitivity and specificity, but it is a daunting task to keep up with all of the immunohistochemistry-related papers published. Finally, Web sites of a number of quality assurance organizations are accessible and can provide a wealth of information comparing the "real world" performance characteristics of different antibodies to the same target protein. False-positive signals can result from a number of factors, including the use of inappropriately high antibody concentration, and "pseudospecific" signal that is in the wrong compartment of the cell. False-negative signal can result from factors such as use of a nonoptimized epitope retrieval method. It is critical that epitope retrieval methods be optimized for each antibody employed in the laboratory. CONCLUSIONS: -By paying attention to these potential problems, the "black box" of diagnostic immunohistochemistry can be made more transparent.
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 | 0.073 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".