Addressing pain in people living with cystic fibrosis: Cystic fibrosis foundation evidence-informed guidelines
Bibliographic record
Abstract
• Pain is common in CF and impacts health, function, and quality of life. • People living with CF desire effective, accessible pain management. • Effective pain management in CF requires an individualized, multimodal approach. • CF care teams and other specialists should collaborate to provide pain management. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".