Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS), a neurodegenerative disease, is fatal within 3 years of symptom onset. Both upper motor neurons and lower motor neurons are targeted. It is hypothesized that: edaravone is effective at managing ALS. This review article used a combination of secondary and primary research articles to gain a plethora of information to help test this hypothesis. Using PubMed, research articles were studied to identify important information. For the Introduction, both secondary and primary articles were used without a limitation on publication date. For the Results section, only primary articles were used which had to have been published no earlier than 2006. The Results section of this review helped to support the hypothesis that edaravone is effective at managing ALS. The most pivotal efficacy endpoint, the change in the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised score, was positively influenced by edaravone over the placebo. This was shown to be statistically significant by use of analysis of variance, amongst other statistical tests. Secondary endpoints such as forced vital capacity and pinch strength were also analyzed, showing similar favorable results. From the clinical trials analyzed in this review, it is concluded that edaravone is sufficient in treating ALS. Edaravone is limited to a target population which could prove to be a problem. Future studies should explore this issue in hopes of expanding the treatment population of edaravone. J Neurol Res. 2020;10(5):150-159 doi: https://doi.org/10.14740/jnr589
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".