Epidemiological Surveillance of Amyotrophic Lateral Sclerosis: A Review
Bibliographic record
Abstract
Abstract Background Registries and clinical databases are important tools to systematically record and collect information about individuals with rare diseases and to monitor disease patterns in populations. Through a review of the published literature on strategies used for surveillance of Amyotrophic Lateral Sclerosis (ALS), our objective was to better delineate the varied approaches used to monitor ALS at a population level. Further, we sought to determine the potential of registries to enhance knowledge on the epidemiology of ALS using a case study comparing epidemiological outputs from registries in the United States, United Kingdom, and Italy. Summary We searched Medline, Embase, Global Health, PsycInfo, Cochrane Library, and CINAHL identifying articles published between January 1 st , 2010, and May 12 th , 2021. Studies describing population registries, cohorts of individuals with ALS, or large-scale studies aimed at systematically identifying people with ALS, were eligible for inclusion. 1,447 publications were found, of which 141 were selected for full text review, and 41 of those were selected for data extraction. We identified ALS registries and pertinent databases in 4 continents (North America, Europe, Asia, and Oceania). Stated objectives of the registries/databases shaped their framework, methodology, and follow-up. The US National Registry demonstrates substantial research outputs and methodological strengths, producing many descriptive epidemiological outputs (n=5 studies) and several methodological papers (n=12 studies). The UK and Italy overall each produced a similar number of studies (albeit with fewer methodological papers), across several different registries and regions. Key Messages Due to challenges inherent to the surveillance of rare diseases, registries are a vital tool in determining and assessing the global impact of ALS. Nevertheless, the development and implementation of registries is not feasible everywhere in the world. There are advantages and drawbacks to structuring registries at a national or regional level, often dictated by funding availability, resources and health care infrastructure, and research objectives. To fully assess the epidemiological burden of ALS globally, collaborative initiatives are needed to fill gaps in knowledge, and there is a critical need to harmonize and optimize the development, collection, and sharing of data across registries.
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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".