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16/100

⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Treatments

Topic: Plasma and spinal-fluid biomarkers

Background Efficient cerebral microhemorrhage (MCH) monitoring is critical for anti-amyloid therapy safety due to ARIA-H risk. We developed MCH-Guard, a multimodal machine-learning framework, to stratify MCH risk using ADNI data (N=813). Methods Nested models integrated clinical history, fluid biomarkers, and imaging to predict MCH presence, incidence, and stability. Results The comprehensive model detected baseline MCH with high accuracy (AUC 0.86). Notably, the “minimal” model (M1), utilizing only demographics and clinical history, achieved robust performance (AUC 0.82). Longitudinal models predicted time-to-onset (R 2 =0.68) and stratified four-year risk. Furthermore, we identified a transient vascular instability phenotype—where MCH status fluctuates— which was strongly predicted by hepatic factors. Conclusions MCH-Guard offers a flexible clinical decision-support tool for optimizing spontaneous MCH & ARIA surveillance. The strong performance of the clinical-only M1 model supports equitable risk assessment in resource-limited settings, while the characterization of vascular instability addresses a critical confounder in safety monitoring.

Key findings

Results The comprehensive model detected baseline MCH with high accuracy (AUC 0.86).

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Study details

Study designlongitudinal study
Participants813
Effect sizeAUC 0.86; AUC 0.82
Evidence strengthModerate

How this study scored 16/100

Preprint — not yet peer-reviewed. It scored 48 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 30 of the 62 points we could judge it on, which is 48 out of 100.

Component	Points	Why
Journal tier	2 / 10	Tier 4 — not a peer-reviewed journal (medRxiv)
Journal's citation record	0 / 4	Not a journal article, so the journal's record does not count
Study design	7 / 12	A longitudinal study following the same people over time
Strength of evidence	3 / 5	Rated “moderate” in our summary
Peer-review validation	4 / 10	Preprint — not yet peer-reviewed. Hosted on a preprint server.
How recent	5 / 5	2026 — within 1 year(s) of the newest study here
Sample size	2 / 4	813 participants
Effect reported	3 / 3	Effect reported with a statistic (interval, p-value, ratio or similar)
Studied in people	3 / 3	Studied in people (participant count stated)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	— / 7	No citation count (not found in OpenAlex) — left out of this score
Citations per year	— / 5	No citation count or no year — left out of this score
Impact for its field and year	— / 6	No field-weighted citation impact (OpenAlex has none yet) — left out of this score
Cited by studies here	— / 6	Not yet checked against this library's citation graph — left out of this score
Still being cited	— / 4	No yearly citation counts — left out of this score
Senior author's track record	— / 4	Senior author's record not available — left out of this score
Institution's track record	— / 3	No institution listed in OpenAlex — left out of this score
Multi-centre collaboration	— / 3	Institutions not listed — left out of this score
Indexed in PubMed	1 / 2	Not in PubMed, but has a DOI
Free to read	0 / 2	Behind a paywall — scores zero
Points	30 / 62	Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 30 of the 62 points we could judge it on is 48 out of 100.
Total	48 / 100	Capped to 16 because this is not a peer-reviewed article.

What each of these 20 components measures, and what this score is not →

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Cite this study APA, Vancouver, BibTeX · download .bib or .ris

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APA 7th

Gel, A., Phillips, E., Hausle, I., Thropp, P., Tosun, D., & Alzheimer’s Disease Neuroimaging Initiative. (2026). MCH-Guard: Multimodal Machine Learning Framework for Risk Stratification of Cerebral Microhemorrhage Risk in the Alzheimer’s Disease Neuroimaging Initiative. medRxiv. https://doi.org/10.64898/2026.06.18.26355972

Vancouver

Gel A, Phillips E, Hausle I, Thropp P, Tosun D, Alzheimer’s Disease Neuroimaging Initiative. MCH-Guard: Multimodal Machine Learning Framework for Risk Stratification of Cerebral Microhemorrhage Risk in the Alzheimer’s Disease Neuroimaging Initiative. medRxiv. 2026. doi:10.64898/2026.06.18.26355972.

BibTeX

@article{gel2026-87340723,
  author  = {Gel, A. and Phillips, E. and Hausle, I. and Thropp, P. and Tosun, D. and {Alzheimer’s Disease Neuroimaging Initiative}},
  title   = {{MCH-Guard: Multimodal Machine Learning Framework for Risk Stratification of Cerebral Microhemorrhage Risk in the Alzheimer’s Disease Neuroimaging Initiative}},
  journal = {medRxiv},
  year    = {2026},
  doi     = {10.64898/2026.06.18.26355972},
  url     = {https://doi.org/10.64898/2026.06.18.26355972}
}

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