Reply to: Broadening concepts of core pathobiology in various aspects of COPD development
Bibliographic record
Abstract
We thank E.H. Walters and co-workers for highlighting the innovation of our study [1] using micro-computed tomography (micro-CT) imaging to match tissue pathology with its gene transcriptome, in order to understand the pathobiology of small airway disease in end-stage COPD. We agree with the authors that post-transplant, explanted COPD lungs do represent the end stages of the disease; however, such lung samples provide the only opportunity to assess the entire lung structure and the heterogeneity of small airway disease and emphysema across lung height without the presence of cancer. As the authors note, we have previously shown that destruction of the last generation of small conducting airways, the terminal bronchioles, precedes emphysematous tissue destruction in end-stage COPD [2]. More recently, this work has been validated using formalin-fixed-paraffin embedded (FFPE) samples from patients with mild and moderate COPD, which demonstrated over 41% of their terminal bronchioles are destroyed prior to the detection of emphysema on clinical CT or microscopically on micro-CT [3]. These data highlight that loss of terminal bronchioles occurs early in the disease process and understanding the pathobiology of terminal bronchiole reduction has the potential to develop new therapeutics for COPD. Understanding the pathology of COPD by assessing the “hot spots” or earliest regions of small airway disease
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.024 | 0.054 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".