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Record W4281486122 · doi:10.31579/2578-8868/233

The Predictive Value of Serum Alkaline Phosphatase Level on the Degree of Neurological Impairment in Patients with Acute Cerebral Infarction

2022· article· en· W4281486122 on OpenAlexaff
Chao Han

Bibliographic record

VenueNeuroscience and Neurological Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicAlkaline Phosphatase Research Studies
Canadian institutionsHealth Care Foundation
FundersStrong
KeywordsMedicineCerebral infarctionInternal medicineInfarctionGastroenterologyAlkaline phosphataseCorrelationMyocardial infarctionIschemia

Abstract

fetched live from OpenAlex

Objective: To analyze the correlation between the changes of ALP level and the degree of neurological impairment in patients with acute cerebral infarction. Methods: A total of 267 patients with acute cerebral infarction were selected as the cerebral infarction group, 181 elderly patients who were matched age and gender in the same period with the cerebral infarction group were selected as the control group by the physical examination. All the selected patients were tested for serum ALP, ALT, AST, Cr, BUN,TG, TC, LDL - C and HDL - C after eight hours on an empty stomach. In the 72nd hour of the patient's course of cerebral infarction, the degree of neurological impairment was assessed using NIHSS score. The relationship between serum ALP level and NIHSS score was analyzed. Results: According to NIHSS score, the patients with score of 5~15 were Group A , patients with score of 15~20 were Group B and score of 21~42 were Group C. Pearson correlation analysis showed that the serum ALP level of the three groups was positively correlated with NIHSS score. Multivariate regression analysis results showed that the high serum ALP level of the risk of cerebral infarction is a low serum ALP level 1.58 times. Conclusions Serum ALP level was increased in patients with acute cerebral infarction and was closely related to the degree of neurological impairment. Perhaps the serum ALP level may be used as a serum marker to predict the degree of neurological impairment in patients.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.009
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.058
GPT teacher head0.278
Teacher spread0.220 · 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
Published2022
Admission routes1
Has abstractyes

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