Interim analysis of patients with ILD enrolled in the STARLINER study
Bibliographic record
Abstract
Introduction: The STARLINER study (NCT03261037) uses a novel digital collaboration platform and aims to assess disease behaviour during the peri-diagnostic period in patients with suspected ILD. Insights from STARLINER may facilitate early, accurate diagnosis and reduce patient travel. Here we present data from a predefined interim analysis. Methods: Outcomes include FVC measured using daily home spirometry, daily accelerometry, and FVC, 6MWD and PROs measured in hospital. The digital collaboration platform allows clinicians to access and share data between centres for virtual multidisciplinary discussion. Results: By 5 Oct 2018, 135 patients were enrolled. At interim analysis, 38 patients were diagnosed with ILD: IPF n=25; non-IPF ILD n=13. The Table shows baseline data. Median (range) time from baseline to diagnosis was 5.1 (1–15) and 6.6 (1–32) weeks for IPF and non-IPF ILD, respectively. Median (Q1–Q3) change from baseline to diagnosis for hospital FVC and 6MWD, respectively, was −10.0 mL (−140.0–40.0; n=21) and 0 m (−70.0–40.0; n=17) for IPF and 0 mL (–40.0–90.0; n=13) and −10.0 m (−25.8–10.0; n=7) for non-IPF ILD. Conclusions: Use of the digital collaboration platform is feasible for community and tertiary sites, and home assessments are generally well accepted by patients. So far, the observed disease behaviour aligns with clinical expectations. Data from all enrolled patients with longer follow-up will be available for the ERS meeting.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".