The use of lambs chests in chest drain insertion simulation
Bibliographic record
Abstract
In an attempt to familiarize medical trainees with chest drain insertion, we sought to find a successful way of instructing students with its insertion technique. Students were given a thirty-minute lecture on chest anatomy, indications for insertion and then the insertion was demonstrated. Students were then taught the Seldinger (tube over guide-wire) technique and also the surgical drain (directly-visualized) insertion technique in a one-hour lab session. No direct comparison was made between other popular simulation mediums (such as plastic models or pork back ribs) nor then were students subsequently directly observed inserting tubes on actual patients. The students were instructed then monitored and scored on successful insertion in the pleural space by an examiner informally. In discussion it was felt that the tactile feedback from the lamb's carcasses demonstrated outstanding anatomical correlation and also demonstrated similar difficulties to human chest drain insertion. Both the anterior & mid-axillary lines were clearly visualized as well as the costal margins. There was also the opportunity to demonstrate administration of local anaesthetic agents as well as one of the most common pitfalls of chest tube insertion: hitting the bone. The session was received very well with feedback revealing an appropriate amount of time spent on both instruction and demonstration. Lamb's thoraces are a superb medium for simulation-based instruction of chest tube insertion. The use of an animal model has its benefits with respect to anatomical realism and the tactile realism of feeling actual muscle and bone. It likely provided a good stepping-stone between abstract classroom insertion instruction and hospital patient insertion.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".