Evidence map›Paper›PMID 42230879›Full record

Trial reportScientific reports2026

Motor-dominant symptoms predict persistent neurological impairment in mild-to-moderate degenerative cervical myelopathy: a multi-state modeling study.

Bo Xu, Guo-Liang Ma, Zhi-Zhuang Wang, Xin Chen, Bo-Wen Yang, Xiao-Kuan Qin, Dong-Sheng Yuan, Peng-Hui Li, Ren-Kun Zhao, Jia-Cheng Zheng and 4 more

Abstract readMulticenter StudyRandomized Controlled Trial
In one paragraph

Trial report in Scientific reports, 2026. 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

14 authors.

Bo XuDepartment of Spine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China.
Guo-Liang MaDepartment of Spine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China.
Zhi-Zhuang WangDepartment of Spine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China.
Xin ChenDepartment of Spine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China.
Bo-Wen YangDepartment of Spine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China.
Xiao-Kuan QinDepartment of Spine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China.
Dong-Sheng YuanDepartment of Spine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China.
Peng-Hui LiDepartment of Spine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China.
Ren-Kun ZhaoDepartment of Spine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China.
Jia-Cheng ZhengDepartment of Spine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China.
Min-Shan FengDepartment of Spine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China.
Jie YuDepartment of Spine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China. yujie226@sohu.com.
Li-Guo ZhuDepartment of Spine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China. Zhlg95@aliyun.com.
He YinDepartment of Spine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China. milkywaymoon@163.com.

Funding

China Academy of Traditional Chinese Medicine basic scientific research business expenses outstanding young scientific and technological personnel training special standard project ZZ14-YQ-022National Key Research and Development Program of China 2024YFC 3507401
6 · The paper itself

Abstract

To characterize symptom domain-defined trajectories in mild-to-moderate degenerative cervical myelopathy (DCM) during scheduled non-operative follow-up, quantify transition probabilities and sojourn durations using a continuous-time multi-state Markov (MSM) model, and explore latent clinical heterogeneity through latent class analysis (LCA). This study is a secondary analysis of an existing multicenter randomized, double-blind trial dataset and does not evaluate comparative treatment effects. We conducted a secondary analysis of longitudinal data from a multicenter randomized, double-blind trial dataset (ChiCTR-INR-16009723) involving adults with mild-to-moderate DCM in the archived analytic dataset and complete Japanese Orthopaedic Association (JOA) domain data. The present analysis was restricted to the scheduled non-operative follow-up period and was designed to describe symptom-domain trajectories rather than treatment efficacy. Neurological status was classified into six mutually exclusive states based on JOA domains: asymptomatic; sensory-only impairment; isolated upper-extremity motor deficit; isolated lower-extremity motor deficit; combined upper- and lower-extremity motor deficits with normal bladder function; and sphincter involvement. Visits used in the present analysis took place at baseline; weeks 1, 2, 4, 6, and 8; and months 3 and 6. Transition intensities, 1-year transition probabilities, and mean sojourn lengths were estimated using a continuous-time MSM. Covariate effects (sex, age group, BMI category, prior conservative treatment, comorbidities) were modeled only for transitions meeting the prespecified ≥ 5-event threshold. LCA was performed using repeated state indicators across the scheduled visits to identify data-driven trajectory classes. This secondary analysis included only participants who remained on non-operative management throughout the analyzed follow-up period; patients who underwent surgery during treatment or follow-up were excluded at the data-screening stage. Ninety patients (mean age 58.6 years; 61.1% male) exhibited 71 observable state shifts, with the majority of assessments indicating stability. Sensory-predominant states exhibited considerable dynamism, characterized by frequent bidirectional transitions between asymptomatic and sensory-only states, with very brief mean sojourn durations (~ 0.25-0.31 years). Conversely, motor-dominant phenotypes exhibited enhanced short-term persistence; isolated lower-extremity motor deficits revealed the longest mean sojourn time (~ 2.76 years) and the highest one-year persistence (~ 71.9%). The advancement to more severe conditions over one year was infrequent (< 11%), but a significant proportion exhibited improvement from combined motor impairment to less severe stages. Covariate effects were largely unclear owing to limited occurrences; however, prior medication history and elevated BMI exhibited the most robust relationships with certain transitions. LCA supported a three-class solution (entropy ~ 0.99) including diverse longitudinal severity patterns, roughly differentiating between milder/fluctuating and more motor-dominant, more persistently severe trajectories. In non-operatively managed mild-to-moderate DCM, the clinical course during scheduled follow-up appeared non-linear and phenotype-dependent: sensory-only impairment tended to fluctuate with brief sojourn times, whereas motor deficits, especially lower-extremity motor deficits, were more persistent and showed limited spontaneous return to asymptomatic status. These exploratory MSM and LCA findings may help identify patients who warrant closer monitoring, but they should not be interpreted as establishing surgical timing thresholds or proving benefit from earlier intervention.

Indexed as

Cervical VertebraeSpinal Cord DiseasesAgedDouble-Blind MethodFemaleHumansLatent Class AnalysisLongitudinal StudiesMaleMarkov ChainsMiddle AgedDegenerative cervical myelopathyDisease progressionLatent class analysisMulti-state Markov modelSymptom domains

Identifiers

PMID42230879
PMCPMC13350821

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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.