Environmental, Inhaled and Ingested Causes of Pulmonary Fibrosis
Bibliographic record
Abstract
Pulmonary fibrosis is a general term that refers to a collection of connective tissue around alveolar structures. There are over 200 disorders where the lungs may be involved in a fibrotic response. To determine the cause of pulmonary fibrosis requires an in depth understanding of the pathogenesis of pulmonary fibrosis and breadth of knowledge of the causative agents and associated disorders that may lead to pulmonary fibrosis. A comprehensive evaluation of the patient is absolutely essential, starting with detailed history that includes an occupational and environmental history because fibrogenic exposures can occur in many settings. Equally important is a history of ingestion of pharmaceutical or nonpharmaceutical substances. A physical examination and judicious investigations are always a part of any comprehensive clinical assessment but they are not commonly helpful in elucidating the cause of most pulmonary fibrotic disorders. Although, a chest film is invariably done, a logical and strongly recommended next step is a high-resolution computed tomography (HRCT). HRCT provides a better assessment of the radiological pattern, may suggest a diagnosis as well as direct the site, and type of lung biopsy. If the history and investigations do not lead to a diagnosis then a lung biopsy is required. Prevention or removal of the inciting agent is critical to the treatment of these disorders and in some instances corticosteroids may be of help.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".