Positron Emission Tomography to Improve Assessment of Interstitial Lung Disease in Patients With Systemic Sclerosis Eligible for Autologous Stem Cell Transplantation
Bibliographic record
Abstract
Positron emission tomography (PET) is a promising technique to improve the assessment of systemic sclerosis associated interstitial lung disease (SSc-ILD). This technique could be of particular value in patients with severe diffuse cutaneous SSc (dcSSc) that are possibly eligible for autologous hematopoietic stem cell transplantation (aHSCT). aHSCT is a potentially effective therapy for patients with severe dcSSc and ILD, leading to stabilization or improvement of lung function. However, there is a high need to improve patient selection, which includes (1) the selection of patients with rapidly progressive ILD for early rather than last-resort aHSCT (2) the prediction of treatment response on ILD and (3) the understanding of the mechanism(s) of action of aHSCT in the lungs. As previous studies with 18F-FDG PET in SSc-ILD and other forms of ILD have demonstrated its potential value in predicting disease progression and reactivity to anti-inflammatory treatment, we discuss the potential benefit of using this technique in patients with early severe dcSSc and ILD in the context of aHSCT. In addition, we discuss the potential value of other PET tracers in the assessment of ILD and understanding the mechanisms of action of aHSCT in the lung. Finally, we provide several suggestions for future research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".