Comparison of Monoclonal Versus Polyclonal Calretinin Antibodies for Immunohistochemical Diagnosis of Malignant Mesothelioma
Bibliographic record
Abstract
Of putative specific markers for diffuse malignant mesothelioma, nuclear staining with Zymed polyclonal calretinin antibody has shown the best specificity to date for epithelial diffuse malignant mesothelioma versus adenocarcinoma. We compared specificity and sensitivity of this polyclonal antibody for diagnosis of diffuse malignant mesothelioma with a new monoclonal antibody from DAKO. One hundred eighteen adenocarcinomas and 111 diffuse malignant mesotheliomas-70 epithelial, 22 sarcomatous, and 19 biphasic-were immunostained with calretinin antibodies from Zymed (polyclonal rabbit, prediluted, PAD:DC8) and DAKO(monoclonal mouse, 1:100, clone DAK Calret 1) using manufacturer-recommended procedures. Cases were blinded and assessed for nuclear versus cytoplasmic staining, percent positive cells, and background. Both antibodies showed similar positive predictive values for diffuse malignant mesothelioma by nuclear staining (Zymed=95%; DAKO=97%). False positives in 4 (3.4%) and 2 (1.7%) adenocarcinomas, respectively, stained greater than 10% of cells. Sensitivity for epithelial malignant mesothelioma was slightly less for DAKO antibody (Zymed=80%; DAKO=73%). Neither antibody performed well on sarcomatous malignant mesothelioma (Zymed=2/22; DAKO=1/22). Both antibodies are useful in the diagnosis of epithelial malignant mesothelioma, although monoclonal antibody is slightly less sensitive.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".