Patient-reported burden associated with pheochromocytoma/paraganglioma diagnosis
Bibliographic record
Abstract
Pheochromocytoma and paragangliomas (PPGLs) originate from the chromaffin cells of the adrenal medulla or neural crest progenitors outside the adrenal gland, respectively. The estimated annual incidence of PPGL is between 2.0 and 8.0/million adults. Minimal data exist on the impact of PPGL from the patient's perspective. Therefore, a survey was adapted from a previously published study on gastroenteropancreatic neuroendocrine tumors to explore the voice of patients with PPGL and learn ways to improve clinical care while understanding the current gaps to direct future research. A self-reported online survey was available to patients with PPGL and those with genetic predisposition even without PPGL from June to July 2022. Survey questions captured sociodemographic and clinical characteristics, the diagnostic workup, treatment and monitoring, quality and access to care, and financial impact. Here, we report the most relevant findings on patient experience of disease burden following diagnosis. A total of 270 people responded, the majority of whom were from the USA (79%), Caucasian (88%), and female (81%). The results of this survey highlight the burden of disease on a patient's daily life, resulting in moderate to severe financial distress, increased travel time to specialized facilities resulting in loss of work and wages, and significant delays in care. Respondents reported being unheard and unacknowledged. With a median time to diagnosis just over 2 years, the physical, mental, and emotional toll are substantial. Increasing access to PPGL specialists and centers could lead to faster diagnoses and better management, which may reduce the burden on both patients and healthcare centers.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".