Fiberoptic Oral Intubation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Previous studies have indicated that fiberoptic orotracheal intubation (FOI) skills can be learned outside the operating room. The purpose of this study was to determine which of two educational interventions allows learners to gain greater capacity for performing the procedure. METHODS: Respiratory therapists were randomly assigned to a low-fidelity or high-fidelity training model group. The low-fidelity group was guided by experts, on a nonanatomic model designed to refine fiberoptic manipulation skills. The high-fidelity group practiced their skills on a computerized virtual reality bronchoscopy simulator. After training, subjects performed two consecutive FOIs on healthy, anesthetized patients with predicted "easy" intubations. Each subject's FOI was evaluated by blinded examiners, using a validated global rating scale and checklist. Success and time were also measured. RESULTS: Data were analyzed using a two-way mixed design analysis of variance. There was no significant difference between the low-fidelity (n = 14) and high-fidelity (n = 14) model groups when compared with the global rating scale, checklist, time, and success at achieving tracheal intubation (all P = not significant). Second attempts in both groups were significantly better than first attempts (P < 0.001), and there was no interaction between "fidelity of training model" and "first versus second attempt" scores. CONCLUSIONS: There was no added benefit from training on a costly virtual reality model with respect to transfer of FOI skills to intraoperative patient care. Second attempts in both groups were significantly better than first attempts. Low-fidelity models for FOI training outside the operating room are an alternative for programs with budgetary constraints.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it