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Record W4417145056 · doi:10.3389/fneur.2025.1689318

Study on the intervention effect of mindfulness-based stress reduction on postoperative cognitive dysfunction and psychological resilience in lung cancer patients

2025· article· en· W4417145056 on OpenAlexaboutno aff
Xiaoli Ji, Haiyan Ding, Yingtao Meng

Bibliographic record

VenueFrontiers in Neurology · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionIntervention (counseling)Lung cancerMindfulness-based stress reductionReduction (mathematics)Psychological interventionStress reduction

Abstract

fetched live from OpenAlex

Background Postoperative cognitive dysfunction and psychological problems seriously affect the quality of life and recovery of lung cancer patients. As an emerging psychological intervention method, mindfulness-based stress reduction (MBSR) has been widely recognized and applied in the medical field. Objectives Evaluating the effectiveness of MBSR as an intervention for postoperative cognitive dysfunction and psychological resilience in lung cancer patients. Methods A total of 86 patients who underwent lung cancer surgery in our hospital from January 2022 to December 2023 were enrolled in this study. The research subjects were divided into the control group and the research group by using the method of random number table, each with 43 cases. The control group used conventional care, while the research group used mindfulness-based stress reduction. Differences in outcome indicators between the two groups were assessed through the relevant assessment tools, with statistically significant differences defined as a p -value of < 0.05. Results Before the intervention, there were no significant differences in cognitive functioning, psychological resilience, self-efficacy, cancer-induced fatigue, and sleep quality scores between the two groups ( p > 0.05). After 8 weeks of intervention, Montreal Cognitive Assessment (MoCA), Connor-Davidson Resilience Scale (CD-RISC), and General Self-Efficacy Scale (GSES) scores were elevated in both groups, and in the research group, the MoCA scores (26.23 ± 1.45 vs. 25.05 ± 1.17, p < 0.001), the CD-RISC total score (66.26 ± 8.27 vs. 61.79 ± 7.93, p = 0.012), and GSES score (30.19 ± 3.27 vs. 26.37 ± 2.31, p < 0.001) were significantly higher than those of the control group. In addition, Piper Fatigue Scale score (PFS) and Pittsburgh Sleep Quality Index (PSQI) scores decreased in both groups after the intervention. Behavioral fatigue (4.02 ± 1.28 vs. 4.61 ± 1.37, p = 0.045), emotional fatigue (3.28 ± 1.39 vs. 3.93 ± 1.40, p = 0.033), somatic fatigue (3.81 ± 1.30 vs. 4.47 ± 1.37, p = 0.026), and cognitive fatigue (4.07 ± 1.39 vs. 4.72 ± 1.37, p = 0.031) were significantly lower in the research group than in those in the control group, as was the total sleep quality score (8.63 ± 1.59 vs. 11.12 ± 1.31, p < 0.001). Conclusion MBSR can effectively improve postoperative cognitive function, enhance psychological resilience, and alleviate cancer-induced fatigue, and sleep disorders in lung cancer 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0030.000

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 head0.011
GPT teacher head0.321
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations2
Published2025
Admission routes1
Has abstractyes

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