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Clinical and neuroimaging features of chronic cerebral ischemia in polycythemia vera

2021· article· en· W3146326314 on OpenAlexaboutno aff
G. V. Zyrina, T. A. Slyusa

Bibliographic record

VenueMedical alphabet · 2021
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNeuroimagingInternal medicineCardiologyMontreal Cognitive AssessmentPolycythemia veraCognitive impairmentDiseasePsychiatry

Abstract

fetched live from OpenAlex

The purpose of the study . To study clinical and neuroimaging features of chronic cerebral ischemia (CCI) in polycythemia vera (PV). Materials and methods . 66 patients with PV were examined – the main group (43 men, 23 women; mean age 62.0 ± 3.4 years), of which 64 (97.0%) patients were diagnosed with CCI. The comparison group consisted of 85 patients with CCI (34 men, 51 women; mean age 67.7 ± 4.6 years), who developed against the background of cerebral vascular atherosclerosis and arterial hypertension. To identify cognitive disorders, we used Mini Mental State Examination (MMSE). Insomnia was studied in accordance with the criteria of the International Classification of Sleep ICDS‑22005. The quality of sleep was determined using a questionnaire from the Federal Somnological Center. Neuroimaging (MRI of the brain) was performed on Siemens Symphony 1.5 T and GE Signa 1.5 T tomographs. Results. Subjective symptoms CCI are characterized by a greater representation of asthenic and insomniac disorders. Transient ischemic attacks in patients with PV are significantly more common than in the comparison group, their frequency depends on the duration of PV. The revealed changes in MRI of the brain in the majority of PV patients with CCI are characteristic of multiinfarction vascular encephalopathy; in the comparison group, changes that characteristic for subcortical arteriosclerotic encephalopathy were more often recorded.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.328
Teacher spread0.312 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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