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Record W4362503467 · doi:10.1177/20417314231157004

Application of nanomaterials in the treatment of intracerebral hemorrhage

2023· review· en· W4362503467 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Tissue Engineering · 2023
Typereview
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsIntracerebral hemorrhageMedicineNeuroinflammationOxidative stressIntensive care medicineInflammationSurgeryInternal medicineSubarachnoid hemorrhage

Abstract

fetched live from OpenAlex

Intracerebral hemorrhage (ICH) is a non-traumatic hemorrhage caused by the rupture of blood vessels in the brain parenchyma, with an acute mortality rate of 30%‒40%. Currently, available treatment options that include surgery are not promising, and new approaches are urgently needed. Nanotechnology offers new prospects in ICH because of its unique benefits. In this review, we summarize the applications of various nanomaterials in ICH. Nanomaterials not only enhance the therapeutic effects of drugs as delivery carriers but also contribute to several facets after ICH such as repressing detrimental neuroinflammation, resisting oxidative stress, reducing cell death, and improving functional deficits.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.367
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it