M29 Fvc decline over 1 year predicts mortality but not subsequent fvc decline in patients with ipf
Bibliographic record
Abstract
L Richeldi reports grants and personal fees from Boehringer Ingelheim, during the conduct of the study; grants and personal fees from InterMune, personal fees from Medimmune, personal fees from Biogen-Idec, personal fees from Sanofi-Aventis, personal fees from Roche, personal fees from Takeda, personal fees from ImmuneWorks, personal fees from Shionogi, outside the submitted work. M Kolb reports grants and personal fees from Boehringer Ingelheim, during the conduct of the study; grants and personal fees from Roche, grants and personal fees from Boehringer Ingelheim, personal fees from GSK, personal fees from Gilead, grants from Actelion, grants from Respivert, personal fees from Astra Zeneca, personal fees from Prometic, personal fees from Genoa, grants from Canadian Institute for Health Research, grants from Canadian Pulmonary Fibrosis Foundation, outside the submitted work. A Azuma reports personal fees from Boehringer Ingelheim, outside the submitted work. W Stansen, M Quaresma and S Stowasser are employees of Boehringer Ingelheim. B Crestani reports personal fees and non-financial support from astra-zeneca, grants, personal fees and non-financial support from boehringer ingelheim, non-financial support from cardif, non-financial support from lvl, personal fees and non-financial support from apelis, grants from medImmune, personal fees and non-financial support from sanofi, outside the submitted work.
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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".