Through the Looking Glass and What Was Found There: Imaging Biomarkers of Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Through the Looking-Glass and What Alice Found There (1) describes adventures in a new and alternative world that Alice discovered after stepping through to the other side of a mirror. Importantly, one of this enduring novel’s underlying themes is the presence of inverse reflections and the notion that in the looking-glass world, one’s basic assumptions can be reversed. In a similar manner, in this issue of the Journal, Bodduluri and colleagues (pp. 1404–1410) present a new “through the looking-glass” way of evaluating normal lung regions that, surprisingly, reveals gas trapping not detected using the typical X-ray computed tomography (CT) density thresholds (2). Like the looking-glass adventures, this approach is intuitive and stimulating, and these findings are both clinically relevant and revelatory. Notably, their findings add to the substantial body of work that stems from the Genetic Epidemiology of COPD (COPDGene) study (3), which has improved our understanding of chronic obstructive pulmonary disease (COPD) and provided novel biomarkers of COPD using high-resolution CT. Although COPDGene was designed to identify genetic factors associated with COPD, reports of CT imaging biomarkers as objective measures of disease have dominated, in that nearly half of all COPDGene publications describe CT findings (using PubMed “COPDGene” and “COPDGene and CT”).
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".