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The clinical research of perioperative neurocognitive dysfunction in brain tumor patients

2015· article· en· W3030238922 on OpenAlexaboutno aff
Hongbo Zhang, Linsen Mu, Yanhui Sun, Jiefei Li, Mengkai Li, Bo-yuan Huang, Hui Shen, Shichao Guo

Bibliographic record

VenueChin J Neurol · 2015
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsPerioperativeMedicineNeurocognitiveCognitionBrain tumorPostoperative cognitive dysfunctionPathologicalCognitive declineCognitive impairmentInternal medicineSurgeryPathologyDementiaPsychiatryDisease

Abstract

fetched live from OpenAlex

Objective To study the clinical characteristics of the perioperative cognitive dysfunction in brain tumor patients. Methods One hundred and forty cases of brain tumor patients were evaluated by using the Chinese vesion of Montreal Cognitive Assessment Questionnaire before and after surgery. The distribution and clinical features of cognitive dysfunction in different located lesions and pathological types were analyzed and compared. Results Among the 140 brain tumor patients, the preoperative cognitive impairment and prevalence rate were 59.2%, the score was 24.2±2.4.The postoperative cognitive impairment and prevalence rate were 80.7%, the score was 20.5±4.1.The postoperative cognitive dysfunction prevalence was significantly higher than the preoperative in benign and malignant with infra or supratentorial brain tumor patients(80.0% vs 98.3%, 71.8% vs 92.2%, all P<0.05). Conclusions Vary different extent cognitive impairment was found in brain tumor patients in preoperative period. The postoperative cognitive dysfunction was seriously worse than that in the preoperative period in malignant and supratentorial brain tumors in earlier recovery period. Key words: Brain neoplasms; Cognitive disorders; Perioperative period

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)0.002
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 score0.055
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.117
GPT teacher head0.404
Teacher spread0.287 · 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
Published2015
Admission routes1
Has abstractyes

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Same venueChin J NeurolSame topicIntracranial Aneurysms: Treatment and ComplicationsFrench-language works237,207