A Retrospective, Descriptive Study of Dyspnea Management in a Multidisciplinary Interstitial Lung Disease Clinic
Bibliographic record
Abstract
Background: Fibrotic interstitial lung diseases (F-ILDs) have a high symptom burden with progressive dyspnea as a primary feature. Breathlessness is underrecognized and undertreated primarily due to lack of consensus on how to best measure and manage it. Several nonpharmacologic and pharmacologic strategies are published in the literature, however there is a paucity of real-world data describing their systematic implementation. Objectives: We describe the types of breathlessness interventions and timing of implementation in our multidisciplinary collaborative care (MDC) ILD clinic and the impact of our approach on dyspnea trajectory and acute care use in ILD. Methods: A retrospective, observational study of deceased ILD patients seen in our clinic (2012-2018) was conducted. Patients were grouped by baseline medical research council (MRC) grade and dyspnea interventions from clinic enrolment until death were examined. Healthcare usage in the last 6 months of life was collected through Alberta’s administrative database. Results: Eighty-one deceased ILD patients were identified. Self management advice was provided to 100% of patients. Pulmonary rehabilitation (PR) and home care (HC) referrals were made in 40% and 57% of patients, respectively. Eighty percent were treated with oxygen and 53% with opioids during the study. MDC-initiated referral to PR and HC, oxygen and opioid prescriptions were provided a median of 13, 9, 11, and 4 months prior to death, respectively. Stepwise implementation of interventions was observed more commonly in MRC 1-2 and concurrent implementation in MRC 4-5. Conclusions: Our clinic’s approach allows early and systematic dyspnea management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".