Development and Validation of Diffuse Idiopathic Pulmonary Neuroendocrine Hyperplasia Diagnostic Criteria
Bibliographic record
Abstract
INTRODUCTION: Diffuse idiopathic pulmonary neuroendocrine hyperplasia (DIPNECH) is a rare condition that is likely underdiagnosed owing to the lack of established and validated diagnostic criteria. These clinical guidelines are empirical and created on the basis of a limited number of studies. This study was designed to validate the existing criteria and to identify new clinical parameters that can accurately diagnose DIPNECH. METHODS: Patients with DIPNECH were identified from a cohort that underwent surgical lung resection for pulmonary carcinoids. The study cohort included a total of 105 consecutive cases with neuroendocrine lesions. Initial diagnostic predictors of DIPNECH were selected from the literature. We employed univariate and multivariate models to evaluate the association of clinical, pathologic, radiologic variables with the likelihood of DIPNECH. RESULTS: < 0.05). After adjustment for sampling variations, the ratio of the total number of PNELs to the number of bronchioles was found to be considerably higher in DIPNECH category. Multivariate analysis identified the total number of PNELs and multiple pulmonary nodules (>10) as independent predictors of DIPNECH. The performance of our criteria revealed an accuracy of 76% in detecting DIPNECH cases. CONCLUSIONS: We proposed a set of diagnostic criteria for DIPNECH on the basis of an expert-panel approach integrating pathological features, radiology, and clinical data. Our findings will help identify DIPNECH patients, without a pathological confirmation of a neuroendocrine lesion. Before the implementation of these criteria in clinical practice, they require further validation in multi-institutional cohorts.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".