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Record W2372502202

Montreal cognitive assessment scales applied in screening for cognitive function in patients with hypertensive cerebral hemorrhage

2014· article· en· W2372502202 on OpenAlexaboutno aff
LI Xiao-y

Bibliographic record

VenueClinical Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCognitionInternal medicineCardiologyPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

Objective To investigate the significance of Mo CA in the evaluation of the damage of cognitive function in patients with hypertension cerebral hemorrhage. Methods A total of 76 patients with hypertension cerebral hemorrhage who visited the doctor again after the onset of 6 months to 2 years were selected,and the cognitive function were detected by Mo CA and MMSE. First of all,used the MMSE to evaluate the cognitive function according to the patient education degree corresponding with the critical value of MMSE,selected the hypertension cerebral hemorrhage patients with normal value. For these hypertension cerebral hemorrhage patients,continue to test the cognitive function application of Mo CA,and with Mo CA rating scale,divided them into different groups and compared. Results There were 76 cases with normal value of MMSE,and the MMSE score was( 27. 5 ± 2. 3) points,Mo CA score( 23. 2 ± 4. 2) points,including 19 patients( 25%) with normal Mo CA( ≥26),57 cases( 75%) with anomaly Mo CA( 26),the anomaly Mo CA group had lower scales in visual space and executive ability,naming,note,language,abstract and delayed memory,directional force and cognitive domain score,the differences were statistically significant( P 0. 01). Conclusion Mo CA scale test can reflect the damage clinical characteristics of cognitive function in patients with hypertension cerebral hemorrhage. It can be used as a good screening tool for the damage of cognitive function in patients with hypertension cerebral hemorrhage,and its application value is better higher MMSE.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 integrity0.0000.001
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 head0.040
GPT teacher head0.354
Teacher spread0.315 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2014
Admission routes1
Has abstractyes

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