Assessment of a ventilator circuit adapter to be used in conjunction with the Respimat* Soft Mist Inhaler (SMI)
Bibliographic record
Abstract
Rationale: With the introduction of the Respimat* SMI there is a desire for an adapter to allow its use in ventilator circuits. An adapter (RespiConnect*) was assessed in terms of drug delivery and safety features. Methods: The adapter contains a self-sealing valve and safety cap intended to prevent loss of airway pressure and maintain a closed ventilator circuit. The adapter was assessed for its ability to retain circuit integrity as well as aerosol drug delivery. Each adapter (n=10) was inserted in the inspiratory limb of a heated adult circuit. A 7.0 mm ET tube was attached to a filter and the distal side of the filter coupled to an adult test lung to simulate a patient. Ventilation (600-mL, I:E 1:3, 10 BPM) was created by a Siemens 900C. Five actuations of Combivent® SMI were delivered, each actuation followed by 6 complete breathing cycles. Between actuations the SMI was removed from the adapter and primed according to manufacturer’s instructions. Salbutamol (SAL) and ipratropium (IPR) were recovered from the filter and quantified by HPLC as % label claim (LC). Results: During the experiment the circuit did not lose pressure and no alarms were triggered, indicating that the self sealing valve was effective. Mean values of 30.3% and 29.4% LC were determined for SAL and IPR respectively. Conclusions: Approximately 30% LC of both drug components in the SMI was delivered to the distal end of ET tube via the adapter. The presence of a self-sealing valve and safety cap for the adapter should ensure ventilator circuit integrity and therefore also help protect the safety of the patient.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".