European Respiratory Society clinical practice guideline on symptom management for adults with serious respiratory illness
Bibliographic record
Abstract
Respiratory symptoms are ubiquitous and impair health-related quality of life in people with respiratory disease. This European Respiratory Society (ERS) task force aimed to provide recommendations for symptomatic treatment in people with serious respiratory illness. The ERS task force comprised 16 members, including representatives of people with serious respiratory illness and informal caregivers. Seven questions were formulated, six in the PICO (Population, Intervention, Comparison, Outcome) format, which were addressed with full systematic reviews and evidence assessed using GRADE (Grading of Recommendations Assessment, Development and Evaluation). One question was addressed narratively. An "evidence-to-decision" framework was used to formulate recommendations. To treat symptoms in people with serious respiratory illness, the task force suggests the use of graded exercise therapy (conditional recommendation, low certainty of evidence); and suggests the use of a multicomponent services, handheld fan and breathing techniques (conditional recommendations, very low certainty of evidence). The task force suggests not to use opioids (conditional recommendation, very low certainty of evidence); and suggests either administering or not administering supplemental oxygen therapy (conditional recommendation, low certainty of evidence). The task force suggests that needs assessment tools may be used as part of a comprehensive needs assessment, but do not replace patient-centred care and shared decision making (conditional recommendation, low certainty of evidence). The low certainty of evidence, modest impact of interventions on patient-centred outcomes, and absence of effective strategies to ameliorate cough highlight the need for new approaches to reduce symptoms and enhance wellbeing for individuals who live with serious respiratory illness.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".