Severe Loss of Appetite in Amyotrophic Lateral Sclerosis Patients: Online Self-Assessment Study
Bibliographic record
Abstract
BACKGROUND: Undesirable loss of weight is a major challenge in amyotrophic lateral sclerosis (ALS). However, little is known about loss of appetite in ALS patients. OBJECTIVE: We investigated loss of appetite in ALS patients by means of an online self-assessment and whether ALS-related symptoms were associated with it. METHODS: Loss of appetite in 51 ALS patients was assessed using the Council on Nutrition Appetite Questionnaire (CNAQ). Loss of appetite is defined as a CNAQ-score of 28 or less with a predicted weight loss of at least 5% within 6 months. We developed an Internet portal to facilitate self-assessment. RESULTS: Approximately half of the ALS patients (47%, 24/51) suffered from severe loss of appetite; after 6 months this increased to nearly two-thirds (65%, 22/34). An average weight loss of 5% was found in the group with severe loss of appetite as compared to only 2% of patients with normal appetite. Interestingly, loss of appetite was associated with respiratory dysfunction (P=.001, R(2)=.223). CONCLUSIONS: Loss of appetite was more common and more severe than expected. It was found to be an independent risk factor for unintended weight loss and may be related to dyspnea. The impact of severe loss of appetite on survival and quality of life should be established in further studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".