Understanding patient, caregiver, and healthcare provider perspectives of the management of long-chain fatty acid oxidation disorders
Bibliographic record
Abstract
Long-chain fatty acid oxidation disorders (LC-FAODs) are a group of rare, inherited, metabolic disorders that can lead to a wide range of symptoms that predominantly affect organ systems with high energy needs, such as the heart, liver, skeletal muscle, and nervous system. Clinical management primarily consists of close attention to and monitoring of diet and activity and avoidance of prolonged fasting. In addition, patients and caregivers must be alert for signs of life-threatening metabolic decompensation. As a result, LC-FAODs can have significant and wide-ranging impacts on the lives of patients and their caregivers. This article describes the effects of LC-FAODs at different life stages and in the context of the North American healthcare system from the perspective of a group of patients, caregivers, and healthcare providers ( n = 6). We explain how challenges and needs change throughout life. Following an early diagnosis, an adjustment phase occurs during which caregivers may feel overwhelmed by their new roles and deeply concerned for their children’s futures. As children grow, they become more aware of the differences between themselves and their peers, and with increasing independence comes more responsibility for managing their own condition. Major life events, such as new employment and moving house, pose challenges for people of all ages. In addition, it may be difficult to find and connect with qualified and experienced healthcare providers; navigate the health insurance system; and educate and align primary, specialist, and emergency care providers. We propose several strategies to improve the care of patients with LC-FAODs, such as educating local healthcare teams, improving trust between patients/caregivers and healthcare providers, and raising awareness of the challenges faced by patients and caregivers across the different life stages.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".