A patient journey map for people living with autoimmune pulmonary alveolar proteinosis
Bibliographic record
Abstract
INTRODUCTION: Patients with autoimmune pulmonary alveolar proteinosis (PAP) face a complicated journey (physically, emotionally, and financially) to receive the correct diagnosis and treatment. We developed a patient journey map (PJM) to describe the experiences and needs of patients with autoimmune PAP in the USA. METHODS: This PJM was developed in four stages: (1) analysis of existing literature; (2) patient advisory board meetings (n = 7); (3) an online survey (n = 19); and (4) a validation workshop (n = 6). RESULTS: Four phases of the patient journey were identified: (1) symptoms and experience before diagnosis; (2) diagnosis; (3) treatment; and (4) ongoing monitoring. Patients reported heterogeneous and indirect diagnostic pathways, often waiting months or years for the correct diagnosis. The majority reported at least one misdiagnosis, most commonly pneumonia. Treatment pathways varied substantially, and current treatments and off-label therapies were frequently described as burdensome, emotionally taxing, and/or financially worrisome. Patients described their journey as an "emotional rollercoaster," especially during pre-diagnosis and treatment. Patients reported common barriers to care, particularly insurance problems and access to expert care. Patients specifically cited the need for improved education on autoimmune PAP within the medical community and increased help with insurance challenges related to current treatments. CONCLUSIONS: This PJM provides insights on patients' journeys with autoimmune PAP. Patients reported inconsistent, burdensome, and circuitous journeys. This PJM provides the medical community with valuable information on patients' needs and increases awareness of this rare disease. Over time, these factors may improve diagnosis, treatment, and the holistic experience of patients with autoimmune PAP.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".