Preparedness of China's health care system to provide access to a disease‐modifying Alzheimer's treatment
Bibliographic record
Abstract
INTRODUCTION: Although the majority of patients with Alzheimer's disease (AD) reside in low-and middle-income countries, little is known of the infrastructure in these countries for delivering emerging disease-modifying treatments. METHODS: We analyze the preparedness of China, the world's most populous middle-income country, using desk research, expert interviews and a simulation model. RESULTS: Our findings suggest that China's health care system is ill-prepared to provide timely access to Alzheimer's treatment. The current pathway, in which patients seek evaluation in hospital-based memory clinics without a prior assessment in primary care, would overwhelm existing capacity. Even with triage using a brief cognitive assessment and a blood test for the AD pathology, predicted wait times would remain over 2 years for decades, largely due to limited capacity for confirmatory biomarker testing despite adequate specialist capacity. DISCUSSION: Closing this gap will require the introduction of high-performing blood tests, greater reliance on cerebrospinal fluid (CSF) testing, and expansion of positron emission tomography (PET) capacity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".