Objective assessment of technical skills during orotracheal intubation
Bibliographic record
Abstract
Force during Orotracheal Intubation is an objective measure of Competency Introduction:Force used during Mannequin based intubation has been shown to correlate inversely with the level of expertise and may provide a useful measure for trainee discrimination in a simulated environment.However, it is not known if the force used on the mannequin correlates with that used in the human situation.As simulation may be increasingly used to assess clinical competency and readiness, it is necessary to have valid measures which will be predictive of clinical skills and performance.This study aimed to measure and correlate the force applied during mannequin oro-tracheal intubation with force applied during human oro-tracheal intubation. Methods:A convenience sample of (N=52) health-care professionals filled a self-reported questionnaire and were divided into experts (n=27) and non-expert(n=25) groups based on pre-determined criteria.The study was conducted by placing a super low pressure (LLW, 0.5-2.5 MPa) sensitive film (Prescale; Fujifilm, Madison, WI, USA) on the laryngoscope to measure the force applied to the oropharynx while intubating patients who require general anesthesia.The same procedure was repeated on a mannequin placed outside the operating room under the same circumstances (i.e., Humidity and temperature).The films change in color from white to red in an intensity that correlates with the amount of pressure applied.The films were then analyzed using a pressure distribution mapping system (FPD-8010E; Fujifilm) that calculates the pressure applied over a predefined area and then
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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 | 0.004 |
| 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) | 0.002 | 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".