ArticleScanning2022
Clinical Characteristics and Mathematical Analysis of Curative Effect of Hemodialysis in Curing Poisoning Caused by Snakebite.
Article in Scanning, 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 It has been retracted, and should not be counted.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, 1 citations in OpenAlex.
- Article
Corrections and comments
- Retraction · 2023-06-21Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to explore the clinical characteristics of hemodialysis in curing poisoning from snakebites, a two-classification model of nuclear logistic neural network based on restricted Boltzmann machine is proposed. The model combines kernel logistic regression with artificial neural networks, enabling the model to both learn autonomously and handle linearly inseparable problems. The network first performs feature learning through unsupervised training of restricted Boltzmann machines and obtains the initial values of the parameters to be identified, which reduces the influence of the randomness of the initial parameters. The variable universe learning rate with scaling factor is used to learn the parameters to be identified, and the model convergence speed is improved by dynamic adjustment of the learning rate. Experimental results show the following: Compared with before treatment, patient's activated partial thromboplastin time (APTT) after treatment and the prothrombin time (PT) level decrease, fibrinogen (FIB) levels are elevated, aspartate transferase (AST) and creatine kinase isoenzyme (CK-MB) level decreased, and the differences were statistically significant (
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.