Late Breaking Abstract - Developing a prognostic SLB-derived gene signature in fibrosing ILDs
Bibliographic record
Abstract
Persisting diagnostic uncertainty in ~20% of cases who undergo surgical lung biopsies (SLBs), and significant variability in clinical courses and outcomes for every given subtype of fibrosing ILD are challenges in managing fibrosing interstitial lung diseases (ILDs). In this registered, prospective cohort study ( NCT03836417 ), treatment-naive patients diagnosed with fibrosing ILD based on SLBs and multi-disciplinary discussion, and > 1 year of follow-up were prospectively followed after SLB. The primary endpoint was progression of disease, as defined by the ATS guidelines. RNA-seq was performed on fresh-frozen tissue obtained from the operating room of every SLB. Between June 2019 and June 2023, 44 patients were included in the study. During this period, 14 (32%) progression of disease events were observed, including 7 deaths and 4 lung transplants. Clinical variables were considered (age, gender, BMI, diagnosis of IPF, baseline FVC, DLCO and 6MWD), but only BMI was significantly protective against progression. After variance filtering, we divided our cohort into half to perform training and validation, and resampled 100 times. Penalized Lasso Cox regression was used with leave-one-out cross validation to select genes associated with progression. The median AUC and Harrell’s C-index were 0.777 (SD = 0.09) and 0.743 (SD= 0.13), respectively. The integrated Brier score was 0.158. HLA-B expression predicted progression, while HLA-DQA1, RP11-532E4.2 and TSIX expression was protective in >30 % of resampling. We identified a gene signature obtained from SLBs that predicts progression in fibrosing ILDs at the time of biopsy, agnostic to ILD subtype. We plan to perform an external validation of our findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".