Epidemiology and healthcare impact of IPF in Ontario from 2013-17.
Bibliographic record
Abstract
Introduction: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive disease that may have high morbidity and mortality. There is scarce data on its impact on healthcare. Objective: In this retrospective study, we describe the epidemiology, comorbidities, and healthcare expenditure of patients with IPF in Ontario using billing codes submitted over 5 years. Methods: We identified Ontario residents older than 18 years of age diagnosed with IPF from January 2013 to December 2017. IPF diagnosis was defined as ICD-10-CA billing codes reported on inpatient and outpatient systems, and incident cases as those with IPF codes without a prior IPF code 2 years before. Common comorbidities were identified using ICD-10-CA codes recorded during the study timeframe, and average healthcare expenditures for each patient was calculated 1 year before and after IPF diagnosis. Costs were reported in Canadian dollars adjusted to 2020 and analyzed from a healthcare system perspective. Results: The number of incident IPF cases in Ontario varied from 848-952 per year, and the yearly incidence rates from 8.4 to 9.1 per 100,000 between 2013 and 2017. Most new cases of IPF were diagnosed after the 7th decade of life mainly in males ≥80 years old. The most prevalent comorbidities were diabetes (20.8%), COPD (12.8%) and CHF (8.6%). Patients with IPF required more outpatient resources 1 year after diagnosis compared to 1 year prior but had no significant changes in ER visits or hospitalizations. The mean and median healthcare costs per patient almost doubled 1 year after diagnosis compared to 1 year before diagnosis. Conclusions: IPF significantly increased outpatient healthcare burden and expenditures one year after diagnosis.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".