Trial reportJournal of healthcare engineering2022
Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening.
Trial report in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed, 5 citations in OpenAlex.
- Retracted: Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening.Journal of healthcare engineering · 2023Article
Corrections and comments
- Retraction · 2023-06-28Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Informed/Patient Consent - None/Withdrawn · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The preoperative qualitative and hierarchical diagnosis of intervertebral foramen stenosis is very important for clinicians to explore the effect of multimodal analgesia nursing on pain control after spinal fusion and to formulate treatment strategies and patients' health recovery. However, there are still many problems in this aspect, and there is a lack of relevant research and effective methods to assist clinicians in diagnosis. Therefore, to improve the accuracy of computer-aided diagnosis of intervertebral foramen stenosis and the work efficiency of doctors, a deep learning automatic grading algorithm of intervertebral foramen stenosis image is proposed in this study. The image of intervertebral foramen was extracted from the MRI image of sagittal spine, and the image was preprocessed. 86 patients with spinal fusion treated in our hospital, specifically from May 2018 to May 2020, were randomly divided into the control group (routine analgesic nursing) and the multimodal group (multimodal analgesic nursing), with 43 cases in each group. The pain control effect and satisfaction of the two groups were observed. The results after multimodal analgesia nursing show that the VASs of the multimodal group at different time points were significantly lower than those of the control group (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.