Management of cough in patients with idiopathic interstitial lung diseases in primary care
Bibliographic record
Abstract
IMPORTANCE: Cough is a common symptom in idiopathic interstitial lung diseases (ILDs), there is little information of its management in primary care. The objective of this study was to explore the frequency of cough-related consultations and the medications prescribed to patients with ILDs in primary care. METHODS: This retrospective cohort study used electronic medical records (EMR) from Manitoba primary care providers participating in the Manitoba Primary Care Research Network repository (2014-2019). Cough-related consults and the subsequent medications prescribed to patients with ILDs were identified in the EMR. RESULTS: 295 patients with ILDs were identified, 73 (25%) of them had 141 cough-related consultations (mean 1.9, SD 1.3) during the period studied. In 50 (35%) of the consultations, patients were prescribed one or more of the following: inhaled bronchodilators (34%), nasal corticoids (18%), codeine/opiates (18%), antibiotics (14%), inhaled corticoids (14%), proton pump inhibitors (8%), cough preparations (6%), antihistamines (4%), and oral corticoids (2%). 13 (26%) subsequent cough-related consultations were identified within 6 months, mainly among patients who were prescribed cough preparations, nasal corticoids, antihistamines, and antibiotics. CONCLUSION: One-quarter of patients with ILDs consulted primary care due to cough, and about a third of them received a prescription to address potentially underlying causes of cough. Although further studies are required to explore the effect of the medications prescribed, recurrent cough consultations suggested that cough preparations, nasal corticoids, and antihistamines are among the least effective treatments. More research is needed to understand the causes and optimal treatment of cough in patients with ILDs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".