Anita Seth
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Anita Seth has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Ankush Garg
Department of Anaesthesia and Intensive Care, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
1 shared publication - Kushagra Dua
Department of Anaesthesia and Intensive Care, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
1 shared publication Naresh Dembla
Computer Department, Institute of Professional Studies, Institute of Engineering and Technology, Devi Ahilya Vishwavidyalaya, Khandwa Road Indore, Madhya Pradesh, India
1 shared publication- Ravindra Yadav
Department of Information Technology, Institute of Engineering and Technology, Devi Ahilya Vishwavidyalaya, Khandwa Road Indore, Madhya Pradesh, India
1 shared publication - Rohan Khandelwal
Department of Neuro Anaesthesia and Intensive Care, Max Smart Super Speciality Hospital, New Delhi, India
1 shared publication - Rohini Singh
Department of Anaesthesia and Intensive Care, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
1 shared publication
Latest Publications
- Open Access
- 4 September 2026
- Published by Wolters Kluwer Health in Airway
- Vol. 9 (3) , 159-166
Comparison of Direct and Video Laryngoscopy Using the Same C-MAC® Macintosh Blade for Glottic Visualisation and Intubation Characteristics in Adults Undergoing Elective Surgery: A Prospective Randomised Interventional Study
Background and Aims: Video laryngoscopy (VL) has transformed airway management. However, whether this improved view consistently translates into better intubation performance remains uncertain. The C-MAC ® video laryngoscope uniquely allows both direct laryngoscopy (DL) and VL using the same Macintosh blade. The primary objective was to compare glottic visualisation between DL and VL using the same C-MAC ® Macintosh blade. The secondary objectives included the comparison of intubation characteristics, haemodynamic responses and complications. Methods: A total of 110 patients were recruited, and all 110 were included in the final analysis. All patients underwent glottic visualisation using both DL and VL with the C-MAC ® . Patients were randomised regarding the sequence of laryngoscopy, and the allocated technique was used for tracheal intubation. Glottic visualisation was assessed using the Modified Cormack–Lehane (MCL) grading system and percentage of glottic opening (POGO) score. Secondary outcomes included intubation time, first-attempt success rate, ease of intubation as assessed by the Intubation Difficulty Scale, haemodynamic response and complications. Results: VL resulted in a higher proportion of MCL Grade 1 views (72.73% vs. 54.55%; P = 0.006) and a greater frequency of complete glottic visualisation (POGO score 100: 40.0% vs. 24.55%; P = 0.004). Intubation time, total insertion time and first-attempt success rates were comparable between groups. Conclusions: VL significantly improves glottic visualisation compared with DL in normal airways. The clinical advantage of VL may be more pronounced in anticipated difficult airway situations. The study highlights that improved glottic visualisation alone may not necessarily improve intubation efficiency when blade design and operator factors are standardised.
- 4 September 2026
- Published by Wolters Kluwer Health in Airway
- Vol. 9 (3) , 159-166
Open AccessComparison of Direct and Video Laryngoscopy Using the Same C-MAC® Macintosh Blade for Glottic Visualisation and Intubation Characteristics in Adults Undergoing Elective Surgery: A Prospective Randomised Interventional Study
Background and Aims: Video laryngoscopy (VL) has transformed airway management. However, whether this improved view consistently translates into better intubation performance remains uncertain. The C-MAC ® video laryngoscope uniquely allows both direct laryngoscopy (DL) and VL using the same Macintosh blade. The primary objective was to compare glottic visualisation between DL and VL using the same C-MAC ® Macintosh blade. The secondary objectives included the comparison of intubation characteristics, haemodynamic responses and complications. Methods: A total of 110 patients were recruited, and all 110 were included in the final analysis. All patients underwent glottic visualisation using both DL and VL with the C-MAC ® . Patients were randomised regarding the sequence of laryngoscopy, and the allocated technique was used for tracheal intubation. Glottic visualisation was assessed using the Modified Cormack–Lehane (MCL) grading system and percentage of glottic opening (POGO) score. Secondary outcomes included intubation time, first-attempt success rate, ease of intubation as assessed by the Intubation Difficulty Scale, haemodynamic response and complications. Results: VL resulted in a higher proportion of MCL Grade 1 views (72.73% vs. 54.55%; P = 0.006) and a greater frequency of complete glottic visualisation (POGO score 100: 40.0% vs. 24.55%; P = 0.004). Intubation time, total insertion time and first-attempt success rates were comparable between groups. Conclusions: VL significantly improves glottic visualisation compared with DL in normal airways. The clinical advantage of VL may be more pronounced in anticipated difficult airway situations. The study highlights that improved glottic visualisation alone may not necessarily improve intubation efficiency when blade design and operator factors are standardised.
- Open Access
- 20 April 2024
- Published by Enviro Research Publishers in Current Agriculture Research Journal
- Vol. 12 (1) , 272-285
Optimizing Crop Yield Prediction: Data-Driven Analysis and Machine Learning Modeling Using USDA Datasets
- 20 April 2024
- Published by Enviro Research Publishers in Current Agriculture Research Journal
- Vol. 12 (1) , 272-285
Open AccessOptimizing Crop Yield Prediction: Data-Driven Analysis and Machine Learning Modeling Using USDA Datasets