The Necessity of Data Mining in Clinical Emergency Medicine; A Narrative Review of the Current Literatrue.

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

4 citing papers in PubMed.

1. Normal Variations in Upper Esophageal Sphincter Function During Deglutition: A Secondary Analysis of Videofluoroscopic Data.The Laryngoscope · 2024
   Article
2. Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data mining artificial intelligence.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2021
   Article
3. Data Mining for Cardiovascular Disease Prediction.Journal of medical systems · 2021
   Article
4. Identification of Traditional Chinese Medicine Constitutions and Physiological Indexes Risk Factors in Metabolic Syndrome: A Data Mining Approach.Evidence-based complementary and alternative medicine : eCAM · 2019
   Article

Corrections and comments

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

Authors and funding

4 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

Indexed as