Feasibility of aerosol drug delivery to sleeping infants: a prospective observational study
Bibliographic record
Abstract
OBJECTIVES: Delivery of inhaled medications to infants is usually very demanding and is often associated with crying and mask rejection. It has been suggested that aerosol administration during sleep may be an attractive alternative. Previous studies in sleeping children were disappointing as most of the children awoke and rejected the treatment. The SootherMask (SM) is a new, gentle and innovative approach for delivering inhaled medication to infants and toddlers. The present pilot study describes the feasibility of administering inhaled medications during sleep using the SM. DESIGN: Prospective observational study. SETTING: Out patients. PARTICIPANTS: 13 sleeping infants with recurrent wheezing who regularly used pacifiers and were <12 months old. INTERVENTION: Participants inhaled technetium99mDTPA-labelled normal saline aerosol delivered via a Respimat Soft Mist Inhaler (SMI) (Boehringer-Ingelheim, Germany) and SM + InspiraChamber (IC; InspiRx Inc, New Jersey, USA). OUTCOMES: The two major outcomes were the acceptability of the treatment and the lung deposition (per cent of emitted dose). RESULTS: All infants who fulfilled the inclusion criteria successfully received the SM treatment during sleep without difficulty. Mean lung deposition (±SD) averaged 1.6±0.5% in the right lung. CONCLUSIONS: This study demonstrated that the combination of Respimat, IC and SM was able to administer aerosol therapy to all the sleeping infants who were regular pacifier users with good lung deposition. Administration of aerosols during sleep is advantageous since all the sleeping children accepted the mask and ensuing aerosol therapy under these conditions, in contrast to previous studies in which there was frequent mask rejection using currently available devices. CLINICAL TRIAL REGISTRY: NCT01120938.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".