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Record W2035778488 · doi:10.1177/104063870802000401

Suggested Guidelines for Immunohistochemical Techniques in Veterinary Diagnostic Laboratories

2008· article· en· W2035778488 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Veterinary Diagnostic Investigation · 2008
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsCanadian Food Inspection AgencyShared Health
Fundersnot available
KeywordsMedicineStandardizationImmunohistochemistryTroubleshootingPathologyDermatopathologyVeterinary medicineCompanion diagnosticQuality assuranceExternal quality assessmentComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.037
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.130
GPT teacher head0.411
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it