Clinical impact of an interstitial lung disease Nurse on patients with idiopathic pulmonary fibrosis
Bibliographic record
Abstract
Background: Idiopathic pulmonary fibrosis (IPF) is fatal chronic, progressive fibrosing, irreversible interstitial pneumonia of unknown cause. Studies have proposed a beneficial role of an interstitial lung disease (ILD) Nurse in IPF management. However, there are no studies assessing the impact of ILD Nurse in IPF. Objective: To assess the impact of an ILD Nurse on hospital admissions and emergency department (ED) visits in patients with IPF. Methods: We included all incident cases of IPF diagnosed in our ILD Clinic between May, 2013 and December, 2016 and compared the hospital admissions/ED rates 20 months before and after our ILD Nurse was hired. Logistic regression was used to adjust for potential confounders. Results: We included 59 patients with IPF. Before the ILD nurse was hired 18/34 (53%) patients had at least one hospital admissions/ED visits, compared to 6/25 (24%) after the ILD nurse (OR: 0.281; p-value=0.025). After adjusting for age, gender, FVC, anti-fibrotic treatment, Charlson comorbidity index and Marital status, having an ILD nurse was associated with a significant reduction in the risk of hospital admissions/ED visits (OR: 0.232; p-value=0.019). Low FVC was associated with increased risk of hospital admissions/ED visits (OR: 1.033; p- value 0.047). None of the other variables was associated with hospital admissions/ED visits. Conclusions: Having an ILD nurse significantly decreased the rate of hospital admissions and ED visits.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".