Idiopathic pulmonary fibrosis: another step in understanding the burden of this disease
Bibliographic record
Abstract
The past 25 years have seen a steady increase in the number of studies examining the incidence of idiopathic pulmonary fibrosis (IPF) worldwide [1, 2]. In general, early studies tended to involve clinicians collating cases from their local area [3, 4] or asking interested colleagues to contribute to registries [5, 6], whereas later studies have made use of large databases collected for clinical care or administrative reasons [7–10]. These later studies boasted far greater numbers, though with some concern about the validity of the cases, the reliability of clinical coding and generalisability to the wider population. A recent systematic review estimated the incidence of IPF to be 3–9 cases per 100 000 in Europe and North America, although this included a heterogenous mix of studies with different case definitions and populations, and several less reliable estimates had to be excluded [2]. Therefore, identifying the true incidence of IPF remains a challenge [11]. Idiopathic pulmonary fibrosis: good-quality Canadian study with high incidence adds to the epidemiological jigsaw With thanks to Richard Hubbard (University of Nottingham, Nottingham, UK) for his comments on this work.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.022 | 0.034 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".