Pathologist Survey Reveals Inadequate Awareness of the Importance of High-Quality CD30 Staining in Accurate Diagnosis of T-Cell Lymphoma
Bibliographic record
Abstract
Recent studies have demonstrated the importance of CD30 immunohistochemistry (IHC) in accurate diagnosis of T-cell lymphomas (TCLs) and the significance of CD30 as a therapeutic target. According to NordiQC, an independent scientific organization promoting the quality of immunohistochemistry, the quality of CD30 IHC testing decreased from 2004 to 2011, with 23% of labs achieving borderline or poor results in the latest round of testing. In order to assess awareness of CD30 IHC in TCL diagnosis, and the challenges related to the quality of the IHC procedure, an online survey was conducted among pathologists who diagnose TCLs. 150 pathologists (100 pathologists, 50 hematopathologists) completed an online survey consisting of 49 questions on CD30 IHC and TCL diagnosis. All participants met the following criteria: (1) board certified/eligible, (2) noncommercial pathologist, (3) 3-40 years in practice, and (4) TCL diagnosis made in the last 6 months. 55% of pathologists and 42% of hematopathologists were unaware of the significance of CD30 IHC in TCL classification and 48% of surveyed pathologists did not recognize the importance of TCL subtyping in determining patient treatment. 41% of pathologists were surprised by the significant portion of labs that did not meet the NordiQC staining standards, and 76% indicated that NordiQC results would impact their approach to interpreting referred-in CD30 IHC stained slides. Finally, 38% of hematopathologists and 74% of pathologists were unaware of an FDA-approved anti-CD30 agent. The results of this survey reveal an unmet need to educate pathologists and hematopathologists about the utility of performing high-quality CD30 IHC and the therapeutic significance of CD30 expression in lymphomas, including TCLs. Increasing CD30 IHC awareness among pathologists who diagnose TCLs will enhance accuracy of lymphoma subtyping and subsequent patient care.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".