Relationships between slow vital capacity and measures of respiratory function on the ALSFRS-R
Bibliographic record
Abstract
OBJECTIVE: As declining respiratory muscle function commonly leads to disability and death in amyotrophic lateral sclerosis (ALS), respiratory measurements such as slow vital capacity (SVC) may predict disease progression. This study evaluated the relationship between SVC and symptoms measured by the revised ALS Functional Rating Scale (ALSFRS-R). METHODS: About 453 ALS placebo-treated patients from the EMPOWER trial (NCT01281189) were evaluated. Correlations between %predicted SVC and individual respiratory ALSFRS-R subdomain items, respiratory subdomain score (maximum score of 12), and total ALSFRS-R score (maximum score of 48) were evaluated using the Pearson correlation coefficient. Pearson's chi-squared test was used to evaluate changes from baseline to week 48 in ALSFRS-R respiratory symptom and respiratory subdomain scores in patients with baseline %predicted SVC above/below the median at baseline and with more slowly/more rapidly decreasing %predicted SVC. RESULTS: The %predicted SVC showed significant correlations with dyspnea, orthopnoea, respiratory insufficiency, respiratory subdomain score, and total ALSFRS-R score (all p < 0.0001). Patients with baseline SVC values < median were significantly more likely than those with baseline SVC ≥ median to have a change in total ALSFRS-R respiratory subdomain score from 12 to <12 (40.9% vs. 30.2%, p = 0.0358) and from ≥10 to <10 (41.6% vs. 24.4%, p = 0.0005). Additionally, patients with smaller declines in SVC over time were significantly more likely to have smaller decreases in their respiratory subdomain scores (p < 0.0001). CONCLUSIONS: The higher correlation between %predicted SVC and specific ALSFRS-R symptom scores in patients with rapidly versus more slowly progressing disease reinforces the importance of continually monitoring respiratory function throughout the disease course.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".