Survival analysis of clinical and genetic factors in an amyotrophic lateral sclerosis cohort from China
Bibliographic record
Abstract
To investigate the clinical and genetic factors influencing the survival of amyotrophic lateral sclerosis (ALS) patients in China. Patients were enrolled in the study between December 2013 and December 2018. Clinical variables were recorded upon patient diagnosis. Causative genes related to ALS were screened by whole-exome sequencing and validated by Sanger sequencing. Each patient was followed up every 3–6 months until the endpoint (death or tracheotomy) or the last connection time on 31 December 2020. Propensity score matching analysis was performed to match the genetic and non-genetic ALS patients. The Kaplan–Meier method and multivariable Cox regression were performed for survival analysis. A total of 337 patients, including 32 with genetic ALS and 305 with non-genetic ALS, were enrolled in the study. Before matching, in univariate analysis, age of onset (P < 0.001), site of onset (P = 0.036), diagnostic delay (P < 0.001), ALSFRS-R score at diagnosis (P < 0.001), ΔALSFRS-R (P < 0.001), and causative mutations (P = 0.020) were significant prognostic factors. These factors remained statistically significant after multivariate analysis. After matching, in the multivariate analysis, age of onset (P = 0.003), site of onset (P = 0.014), diagnostic delay (P = 0.007), ALSFRS-R score at diagnosis (P = 0.010), ΔALSFRS-R (P = 0.007), and causative mutations (P = 0.003) were found to be significant prognostic factors. Both clinical factors and genetic factors influenced survival in our ALS cohort. Clarifying of the underlying mechanisms is crucial for the development of future therapies.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".