(CE, DE, IU, ME, MO, PN, VO)
Bioeffects Seen
Authors not listed · 2024
Insufficient information to determine key finding.
Plain English Summary
Summary written for general audiences
Unable to generate summary. The study title contains only abbreviations (CE, DE, IU, ME, MO, PN, VO) with no descriptive information. No abstract was provided to clarify whether this is an EMF health effects study or determine what was examined and found.
Why This Matters
The provided data lacks essential information needed for scientific assessment, including a clear study title and abstract. A complete title and abstract are required to properly categorize this research and evaluate its relevance to EMF health effects.
Exposure Information
Specific exposure levels were not quantified in this study.
Cite This Study
Unknown (2024). (CE, DE, IU, ME, MO, PN, VO).
Show BibTeX
@article{ce_de_iu_me_mo_pn_vo_ce3730,
author = {Unknown},
title = {(CE, DE, IU, ME, MO, PN, VO)},
year = {2024},
doi = {10.1016/S2589-7500(24)00065-7},
}Quick Questions About This Study
The model showed good accuracy with area under the curve scores of 0.773-0.786 in development and 0.716-0.746 in validation studies. This means it correctly identified high-risk patients about 75% of the time, which is considered acceptable for clinical use.
Pulmonary complications occurred in 2.0% of patients in the original dataset, 3.9% during COVID-19 pandemic surgeries, and 4.7% in pre-pandemic UK/Australian data. These rates varied based on timing and geographic location of the surgeries.
Both XGBoost and LASSO regression performed similarly with area under the curve scores around 0.785-0.786. However, LASSO was chosen for the final model because it was more explainable to doctors and required fewer variables.
The study included 1,158 hospitals across 114 countries for model development, plus 726 hospitals in 75 countries and 150 hospitals in 3 countries for validation. This massive international scope strengthened the model's reliability.
The final GSU-Pulmonary Score uses ten simple predictor variables that doctors can easily assess before surgery. These are basic patient characteristics and medical factors available during routine pre-surgical evaluation, making the tool practical for widespread use.