Comparison Of Outcome Between Conventional And Video Laryngoscope In Predicted Difficult Intubation
Bibliographic record
Abstract
Background: Endotracheal intubation is the mainstay of airway management in general anaesthesia. Failure to intubate the trachea, often known as difficult intubation, is always a possibility. To overcome difficult intubation, different methods of instrumental support were developed. Some authors. Methods: From March 2018 to September 2019, a comparative cross-sectional study was conducted at BSMMU, Dhaka in the department of anesthesia, analgesia, and intensive care medicine. A total of 60 patients with predicted difficult intubation who were scheduled for elective surgery under general anaesthesia with endotracheal intubation were selected for the study. Prediction of difficult intubation was assessed by modified Mallampati class III and IV or thyromental height. Results: Time taken from visualization of glottis for insertion of ETT was 12.8±2.3 sec in conventional laryngoscope which was significantly lower in video laryngoscope (15.0±3.6) (p =0.006). Time taken to visualize the glottis was 13.2±1.7 sec in conventional laryngoscope and 13.2±4.0 sec in video laryngoscope (p>0.05). Total time for tracheal intubation was 49.0±6.4 sec in conventional laryngoscope and 53.2±9.9 sec in video laryngoscope (p>0.05). Intubation with first attempt by video laryngoscope was (30/30; 100%) as compared with conventional laryngoscope (27/30; 90%) (p>0.05). Conclusion: It is evident from the study that intubation with video laryngoscope in comparison to conventional laryngoscope might provide better outcome in terms of ease of intubation and number of attempts during intubation for patients undergoing elective surgery under general anaesthesia. Bangladesh Armed Forces Med J Vol 56 No (1) June 2023, pp 7-13
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".