Addressing pain in people living with cystic fibrosis: Cystic fibrosis foundation evidence-informed guidelines
Bibliographic record
Abstract
Even as many outcomes for people living with cystic fibrosis (PLwCF) improve, individuals still experience extensive symptom burdens. From birth, many PLwCF experience both pain as a symptom of their CF disease and procedural pain, posing detriments to health, functioning, and quality of life. Despite its prevalence and impact, there is no CF-specific guidance for the assessment and management of pain. Similarly, no guidance exists regarding communication with PLwCF about their pain experiences or its impact on their lives. Therefore, the Cystic Fibrosis Foundation (CFF) assembled an expert panel of clinicians, researchers, PLwCF, and caregivers to develop consensus recommendations for pain management in CF. We utilized literature review and expert opinion to develop 13 recommendations addressing pain assessment, management, and communication. Recommendations are centered on guiding principles of utilizing a multimodal approach to pain management, offering age and developmentally appropriate assessment and interventions, concurrently treating underlying conditions causing, contributing to, and/or exacerbated by pain, considering societal stigma of the pain experience, particularly for minoritized and marginalized people, and sensitivity to issues of access and cost. These recommendations are intended to guide clinicians in managing pain and improving quality of life for PLwCF with pain at all stages of illness and development.
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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 | 0.014 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".