Evidence map›Paper›PMID 39434862›Full record

ArticleOpen medicine (Warsaw, Poland)2024

Intraoperative changes in electrophysiological monitoring can be used to predict clinical outcomes in patients with spinal cavernous malformation.

Xiaoyu Li, Hongqi Zhang, Jian Ren

Abstract read
In one paragraph

Article in Open medicine (Warsaw, Poland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
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

3 authors.

Xiaoyu LiXuanwu Hospital of Capital Medical University, Beijing, China.
Hongqi ZhangDepartment of Neurosurgery, Xuanwu Hospital, Capital Medical University, China International Neuroscience Institute (China-INI), Beijing, 100053, China.
Jian RenXuanwu Hospital of Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: The study aimed to evaluate the sensitivity and specificity of these monitoring parameters in predicting postoperative neurological dysfunction. Methods: In this study, a total of 85 patients with spinal cavernous malformations (SCMs) treated at Xuanwu Hospital, Capital Medical University, from November 2012 to August 2017 were included. During the surgical procedures, all patients underwent monitoring of motor evoked potentials (MEP) and somatosensory evoked potentials (SEP). The criteria for warning included a reduction of ≥80% in MEP amplitude and ≥50% in SEP amplitude. Results: Among 85 patients, 40 (47.1%) had SCMs located in the thoracic segment, 35 (41.2%) in the cervical segment, 6 (7.1%) in the cervical thoracic segment, and 4 (4.7%) in the lumbar segment. MEP recordings were obtained from 81 patients, and the preoperative McCormick score was 1.53 ± 0.69. The sensitivity of multimodal monitoring combined with the criteria of 80% reduction in MEP amplitude and SEP was 83.9%, with a specificity of 69%, a positive predictive value of 69%, and a negative predictive value of 90.4%. Conclusion: This study emphasizes the crucial role of electrophysiological monitoring, particularly MEP and SEP, during the surgical resection of SCMs. The findings demonstrate that this approach is effective in predicting and preventing postoperative neurological dysfunction, thereby improving patient outcomes.

Indexed as

electrophysiological monitoringintraoperative neuromonitoringmotor evoked potentialssomatosensory evoked potentialsspinal cavernous malformations

Identifiers

PMID39434862
PMCPMC11491772

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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.