Preparation of the Siemens KION Anesthetic Machine for Patients Susceptible to Malignant Hyperthermia
Bibliographic record
Abstract
BACKGROUND: Preparation of anesthetic machines for use with malignant hyperthermia-susceptible (MHS) patients requires that the machines be flushed with clean fresh gas. We investigated the washout of inhalational anesthetics from the KION anesthetic machine. METHODS: In part 1, halothane was circulated through KION anesthetic machines for either 2 or 12 h using a test lung. The times to washout halothane (to 10 parts per million [ppm]) first, from the internal circuitry and second, from the ventilator-patient cassette (without the carbon dioxide absorber) were determined at 5 and 10 l/min fresh gas flow (FGF). In part 2, the rates of washout of halothane or isoflurane from either the KION or Ohmeda Excel 210 machines were compared. The effluent gases were analyzed using calibrated Datex Capnomac Ultima (Helsinki, Finland) and a Miran LB2 Portable Ambient Air Analyzer (Foxboro, Norwalk, CT). RESULTS: Halothane was washed out of the internal circuitry of the KION within 5 min at 10 l/min FGF. Halothane was eliminated from the ventilator-patient cassette in 22 min at the same FGF. The times to reach 10 ppm concentration of halothane and isoflurane in the KION at 10 l/min FGF, 23 to 25 min, was four-fold greater than those in the Ohmeda Excel 210, 6 min. CONCLUSIONS: To prepare the KION anesthetic machine for MHS patients, the machine without the carbon dioxide absorber must be flushed with 10 l/min FGF for at least 25 min to achieve 10 ppm anesthetic concentration. This FGF should be maintained throughout the anesthetic to avoid increases in anesthetic concentration in the FGF.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".