Bibliographic record
Abstract
Der vorliegende Bericht fasst Erfahrungen in BLS mit der Simulationspuppe „Actar 911™” in unserer Klinik seit 1995 zusammen. Positive Berichte aus Kanada und finanzielle Aspekte trugen zur Auswahl bei. Vorteile dieses Systems sind: Anschaffungskosten, Platz sparende Aufbewahrung, keine Ablenkung der ÜbungsteilnehmerInnen durch Anzeigen, einfacher Transport. Intensivere Betreuung unserer InstruktorenInnen glich Nachteile - wie fehlende Manipulationsmöglichkeiten im Gesicht oder keine Anzeige des Übungseffektes (weder Skalen- noch Digitalanzeigen) - ohne Einbuße auf die wirkliche Reanimationssituation aus. Nach vier Jahren Erfahrung folgern wir, dass das „Actar 911™”-Übungssystem für unser Spital einen speditiven Schulungsablauf in einem finanziell akzeptablen Rahmen ermöglicht. Practical BLS Training with „Actar 911” Mannequins We summarize our experience with the basic-life-support mannequin „Actar 911” since 1995. Our department chose this model basing on positive reports from Canada and financial considerations. Advantages of this model are low cost, space saving storage and simple transportability. In addition BLS training course participants were not distracted by displays. Disadvantages of this system are the lacking possibility of manipulation in the face and missing analogue/digital displays for immediate feedback of success. However, a more comprehensive training compensated for these disadvantages without substantial loss of results in real reanimations. After four years of BLS training in our hospital, we conclude that the „Actar 911” system offers effective BLS teaching at an acceptable budget.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".