Orogastric tube insertion using the new gastric tube guide: first experiences from a manikin study
Bibliographic record
Abstract
BACKGROUND: Orogastric tube placement is a common procedure routinely used in clinical anesthesiology and intensive care medicine. Nevertheless high failure rates and severe complications have been reported. We conducted this study to evaluate if the usage of the new gastric tube guide would speed up the placement of orogastric tubes and ease the procedure. METHODS: Thirty one professionals were given a hands-on-training in orogastric tube placement in a simulation manikin without and with the gastric tube guide. Afterwards they performed both methods in randomized order. We recorded the placement time, counted the required attempts and asked the participants to rate their experience with both methods. RESULTS: The median placement time using the gastric tube guide was 14 s compared to 25 s without the device. In addition all participants were able to place the orogastric tube when using the gastric tube guide compared to 26/31 (84%) without it. Furthermore 26/31 (84%) users preferred the gastric tube guide over the standard method. CONCLUSION: Our results show that using the gastric tube guide to place orogastric tubes in a manikin led to a significant shorter placement time and a higher overall success rate.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".