Evidence map›Paper›PMID 41798824›Full record

ArticleFrontiers in neurology

Causal dynamic decision-making for robotic systems in non-Markovian high-difficulty surgery.

Guo Na, Tan Minghui, Li Tiantian, Liu Yang, Zhang Qinjian, Li Yuanxin, Xu Tianlei, Sun Fuchun

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Guo NaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
Tan MinghuiSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
Li TiantianCollege of Information and Electrical Engineering, China Agricultural University, Beijing, China.
Liu YangSchool of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing, China.
Zhang QinjianSchool of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing, China.
Li YuanxinBeijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China.
Xu TianleiBeijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China.
Sun FuchunDepartment of Computer Science and Technology, Tsinghua University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Markov assumption-based surgical decision models cannot account for the time-varying, irregular effects of high-risk intraoperative anomalies such as sudden hemorrhage or inadvertent instrument loss, making them inadequate for specialized procedures like neurosurgery and spinal interventions. To overcome the non-Markovian limitations of conventional surgical process modeling, this study develops a causal modeling framework based on Vector Autoregression (VAR) and Granger causality analysis. The framework constructs a causal chain (original gesture

Indexed as

causal inferencedynamic decision-makingGranger causalitynon-Markov processessurgical robotics

Identifiers

PMID41798824
PMCPMC12963011

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.