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Record W4414103593 · doi:10.36106/paripex/3600124

INTRAOPERATIVE NEUROPLASTICITY: ROLE OF ANESTHETICS IN POSTOPERATIVE COGNITIVE DYSFUNCTION ( POCD) IN YOUNG ADULT

2025· article· en· W4414103593 on OpenAlexaboutno aff
S S Sumith, Nazeer Ahmed Kudligi, Akbar Akbar, Yasmin Babusaheb Pathan

Bibliographic record

VenuePARIPEX INDIAN JOURNAL OF RESEARCH · 2025
Typearticle
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsnot available
Fundersnot available
KeywordsSevofluranePostoperative cognitive dysfunctionPropofolYoung adultNeuroprotectionAnestheticCognitionIncidence (geometry)

Abstract

fetched live from OpenAlex

Background: Postoperative cognitive dysfunction (POCD) is predominantly studied in elderly populations,with limited focus on young adults. This trial investigates the impact of anesthetic techniques (propofol TIVA vs. sevoflurane) on POCD incidence in non-geriatric adults,emphasizing NMDA receptor modulation and neuroinflammation. Methods:In a double-blind randomized trial,20 ASA I-II patients (18–40 years) undergoing elective non-cardiac surgery (>2 hours) were allocated to propofol TIVA (n=10) or sevoflurane (n=10). Cognitive function was assessed preoperatively and at 24h,72h,and 7 days postoperatively using the Montreal Cognitive Assessment (MoCA) and Trail Making Test (TMT-A/B). Serum IL-6 and TNF- levels quantified neuroinflammation. Results: Sevoflurane correlated with higher POCD incidence at 24h (40% vs. 10%, p=0.04) and greater MoCA decline ( MoCA: 2.4±0.8 vs. 0.9±0.6; p<0.001). TMT-B completion time worsened in sevoflurane at 24h ( +28.6±6.2s vs.+8.3±4.1s;p=0.002).Neuroinflammation markers rose significantly in sevoflurane (IL-6: +35.2±4.1 pg/mL vs. +12.1±3.2 pg/mL; p<0.001). Propofol showed faster cognitive recovery by day 7. Conclusion: Propofol TIVA demonstrates superior neuroprotection against POCD in young adults, potentially via attenuated neuroinflammation and NMDA-mediated neuroplasticity modulation

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.032
GPT teacher head0.353
Teacher spread0.322 · 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 designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

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