Management of an Obstructed Tracheostomy in a Limited-Resource Setting
Bibliographic record
Abstract
Obstruction of a tracheostomy tube is a common cause of respiratory compromise in adults and pediatric patients, which can lead to a life-threatening emergency if it is not properly managed. Compromised airway patency has many potential etiologies; however, the scenario described in this technical report focuses specifically on the management of tracheostomy obstruction through the presence of a mucus plug, blood clot, or highly viscous secretions. The simulation described in this technical report was written to be conducted as an in-situ simulation within the intensive care unit (ICU) at Mzuzu Central Hospital, Malawi. The novel aspect of this report is that it depicts the integration of low-tech simulation with a deteriorating patient scenario educational methodology. This integration enables the use of affordable and sustainable simulation materials in Malawi context to deliver learning objectives that are otherwise not achievable. It was designed to train nurses, clinical officers, and nursing students from the ICU and male/female surgical wards. It can be utilized to train similar learners in other resource-poor regions of the world, as well as remote areas of the more developed countries.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".