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Record W3174661374

Application of regional cerebral oxygen saturation monitoring with near-infrared spectroscopy in peri-anesthesia management of elderly hypertensive patients undergoing shoulder arthroscopic surgery.

2021· article· en· W3174661374 on OpenAlexaboutno aff
Zhili Jing, Di Wu

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnesthesiaBispectral indexIntubationTracheal intubationMean arterial pressureHeart ratePostoperative cognitive dysfunctionBlood pressurePerioperativeSurgeryCognitionInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: ) monitoring with near-infrared spectroscopy in peri-anesthesia management of elderly hypertensive patients undergoing shoulder arthroscopic surgery. METHODS: was analyzed. Preoperative and postoperative Mini-Mental State Exam (MMSE), Montreal Cognitive Assessment (MoCA) scores, serum neuron-specific enolase (NSE) and s100β levels were compared between the two groups. The incidence of postoperative cognitive dysfunction (POCD) at 1-3 days was recorded. RESULTS: levels were positively correlated in the two groups (r>0, P<0.05). There were no significant differences in the MMSE or MoCA scores, NSE or s100β levels between the two groups before surgery (all P>0.05). After surgery, the MMSE and MoCA scores of the two groups were decreased (both P<0.05), while the NSE and s100β levels were increased (both P<0.05). The control group showed greater changes in the above four indexes (all P<0.05). The incidence of POCD in the observation group was lower than that of controls at 1, 2, and 3 days after surgery (all P<0.05). CONCLUSION: monitoring with near-infrared spectroscopy in peri-anesthesia management of elderly patients with hypertension undergoing shoulder arthroscopic surgery can effectively stabilize hemodynamics and reduce the incidence of postoperative POCD.

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)0.000
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 score0.064
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.251
Teacher spread0.234 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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