Doc, I Think I Got Something Stuck
Bibliographic record
Abstract
Abstract Introduction One mantra of emergency care is “no airway, no patient.” However, when teaching basic life support and advanced cardiovascular life support (ACLS) management of critical patients, the cardiac support is often emphasized over the airway management. Given the importance of the specific skill of airway management, this simulation focuses on airway management using high-fidelity adult simulation mannequins. Methods This simulation case is designed to last approximately 15-20 minutes. During this time participants must assess the patient and collect a medical history, recognize the presence of a critical airway threat, and take appropriate steps to reclaim the airway. Results This case has had three trial runs with a total of seven medical students. In general the reception from these medical student learners was positive. Overall, they endorsed that the case enhanced their future clinical practice. In response to the question “One thing I learned that I can use to improve my clinical practice,” one participant reported “The fact that McGill forcepts exist [sic]” and another stated “…Also the McGill forceps were helpful to see [sic].” Discussion Initially it was our intention to create this simulation for a pediatric patient, but the overly narrow tracheal and laryngeal diameter of our facility's pediatric mannequin precluded this possibility. The case could be adapted to a child with only minor changes, such as: beginning with a pediatric high-fidelity mannequin and then switching to an airway task trainer or adult high-fidelity mannequin for the steps of airway management; modifying the historical and social factors to be age appropriate.
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.
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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.328 | 0.149 |
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".