Napsin-A Immunohistochemistry in the Diagnosis of Pulmonary Alveolar Proteinosis
Bibliographic record
Abstract
CONTEXT.—: The diagnosis of pulmonary alveolar proteinosis (PAP) relies on a limited set of stains, namely hematoxylin-eosin and periodic acid-Schiff-diastase (PAS-D), demonstrating abundant alveolar material representing mostly surfactant. As cells harboring surfactant also express Napsin-A (pneumocytes and macrophages), we hypothesized that it would also be expressed within alveoli in PAP. OBJECTIVE.—: To evaluate the sensitivity and specificity of Napsin-A in the diagnosis of PAP. DESIGN.—: A 12-year retrospective case control study was designed to identify cases of PAP and potential histologic mimics (intra-alveolar fibrin, pulmonary edema, diffuse alveolar damage, and alveolar mucinosis). PAS-D staining and Napsin-A immunohistochemistry were performed. Distribution and intensity were evaluated by using a semiquantitative 3-point scale. Positivity was defined as 2+ intensity score, regardless of distribution. RESULTS.—: Eleven cases of PAP and 46 control cases were identified. Napsin-A showed positivity in all PAP cases and 3 of 12 cases of edema. Among positive cases, all those with a 2+ distribution were PAP cases, with heterogeneous (1+) staining in all cases of edema. PAS-D showed positivity in all cases of PAP and most controls, except cases of edema. Sensitivity and specificity of Napsin-A for PAP were 100% and 94%, respectively, and of PAS-D for PAP, 100% and 21%, respectively. Double positivity for Napsin-A and PAS-D was 100% specific and sensitive for PAP. CONCLUSIONS.—: This study is the first to demonstrate that Napsin-A is highly specific for the diagnosis of PAP, more so than PAS-D. It also shows that the combined positivity of Napsin-A and PAS-D is 100% specific and sensitive for PAP.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".