Digital Scientific Platform for Independent Content in Neurology: Rigorous Quality Guideline Development and Implementation
Bibliographic record
Abstract
BACKGROUND: Digital communication has emerged as a major source of scientific and medical information for health care professionals. There is a need to set up an effective and reliable methodology to assess and monitor the quality of content that is published on the internet. OBJECTIVE: The aim of this project was to develop content quality guidelines for Neurodiem, an independent scientific information platform dedicated to neurology for health care professionals and neuroscientists. These content quality guidelines are intended to be used by (1) content providers as a framework to meet content quality standards and (2) reviewers as a tool for analyzing and scoring quality of content. METHODS: Specific scientific criteria were designed using a 5-point scale to measure the quality of curated and original content published on the website: for Summaries, (1) source reliability and topic relevance for neurologists, (2) structure, and (3) scientific and didactic value; for Congress highlights, (1) relevance of congress selection, (2) congress coverage based on the original program, and (3) scientific and didactic value of individual abstracts; for Expert points of view and talks, (1) credibility (authorship) and topic relevance for neurologists, (2) scientific and didactic value, and (3) reliability (references) and format. The criteria were utilized on a monthly basis and endorsed by an independent scientific committee of widely recognized medical experts in neurology. RESULTS: Summary content quality for the 3 domains (reliability and relevance, structure, and scientific and didactic value) increased in the second month after the implementation of the guidelines. The domain scientific and didactic value had a mean score of 8.20/10. Scores for the domains reliability and relevance (8-9/10) and structure (45-55/60) showed that the maintenance of these 2 quality items over time was more challenging. Talks (either in the format of interviews or slide deck-supported scientific presentations) and expert point of view demonstrated high quality after the implementation of the content quality guidelines that was maintained over time (15-25/25). CONCLUSIONS: Our findings support that content quality guidelines provide both (1) a reliable framework for generating independent high-quality content that addresses the educational needs of neurologists and (2) are an objective evaluation tool for improving and maintaining scientific quality level. The use of these criteria and this scoring system could serve as a standard and reference to build an editorial strategy and review process for any medical news or platforms.
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 | 0.514 | 0.650 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".