Suggested Guidelines for Immunohistochemical Techniques in Veterinary Diagnostic Laboratories
Bibliographic record
Abstract
This document is the consensus of the American Association of Veterinary Laboratory Diagnosticians (AAVLD) Subcommittee on Standardization of Immunohistochemistry on a set of guidelines for immunohistochemistry (IHC) testing in veterinary laboratories. Immunohistochemistry is a powerful ancillary methodology frequently used in many veterinary laboratories for both diagnostic and research purposes. However, neither standardization nor validation of IHC tests has been completely achieved in veterinary medicine. This document addresses both issues. Topics covered include antibody selection, fixation, antigen retrieval, antibody incubation, antibody dilutions, tissue and reagent controls, buffers, and detection systems. The validation of an IHC test is addressed for both infectious diseases and neoplastic processes. In addition, storage and handling of IHC reagents, interpretation, quality control and assurance, and troubleshooting are also discussed. Proper standardization and validation of IHC will improve the quality of diagnostics in veterinary laboratories.
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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.026 |
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".