Bibliographic record
Abstract
PURPOSE OF REVIEW: Updated clinical practice guidelines for pulmonary fibrosis recommend early integrated palliative care using a primary palliative approach. Clinicians need to be aware of the various palliative interventions in order to implement guidelines' recommendations. This review provides an update on evidence-based palliative therapies. RECENT FINDINGS: Literature review indicates early integration of palliative care in pulmonary fibrosis is feasible and meets patient needs. Key components of a primary palliative approach include comprehensive symptoms and needs screening, systematic symptom management using nonpharmacologic interventions, supplemental oxygen and opioids for dyspnea and cough. Patient-centered communication is essential for successful integration of palliative care. Early, iterative advance care planning in clinic to understand patient goals, values and preferences for current and future care, improves patient care and satisfaction. Prioritizing caregiver inclusion in clinics can address their needs. Collaborating with a multidisciplinary allied team facilitates integration of palliative care and supports patients throughout the disease course. Different models of palliative care delivery exist and can be adapted for local use. The use of artificial intelligence algorithms and tools may improve care and continuity. SUMMARY: Clinicians must develop competency in palliative care. Organizational and policy support is essential to enable seamless integration of palliative care throughout the care continuum.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".