Editorial: Methods and applications in Alzheimer's disease and related dementias
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease worldwide. Currently, AD diagnosis is based on a multidimensional approach that involves clinical, neuropathological examination, and evaluation of biomarkers. The burden of AD is further exacerbated by the fact that brain damage actually begins to develop several years before the diagnosis or even mild cognitive impairment (MCI) is observed. (Long et al. 2019) Therefore, developing early and accurate diagnostic methods are urgently needed. The present Research Topic aims to highlight the latest experimental techniques and methods used to investigate fundamental questions in Alzheimer's disease and related dementias, from integrative functions to molecular and potentially therapeutics.The estimated total healthcare costs for the treatment of Alzheimer disease in 2020 is estimated at $305 billion, with the cost expected to increase to more than $1 trillion as the population ages. (Wong et al. 2020) It is proposed that early diagnosis and intervention are effective ways to reduce the burden of AD. The study by Ren and cols. demonstrates the benefits of a screening program for AD in mainland China and debates the cost-effectiveness of implementing such programs for the health care system. Their report established increase health benefits and reduce the incidence of severe AD and death.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.007 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.018 | 0.026 |
| Insufficient payload (model declined to judge) | 0.019 | 0.021 |
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".