Peak Inspiratory Flow Measured at Different Inhaler Resistances in Patients with Asthma
Bibliographic record
Abstract
BACKGROUND: Patients' peak inspiratory flow rate (PIFR) may help clinicians select an inhaler device. OBJECTIVE: To determine the proportion of patients with asthma who could generate correct PIFRs at different inhaler resistance settings. METHODS: During a UK asthma review service, patients' PIFR was checked at resistance settings matching their current preventer inhaler device, at R5 (high-resistance dry powder inhaler [DPI]) and at R0 (low resistance, pressurized metered dose inhaler [pMDI]). Correct PIFR ("pass") was defined for R5 as 30 to 90 L/min and for R0 as 20 to 60 L/min. A logistic regression model examined the independent predictors of incorrect PIFR ("fail") at R5 and R0. Asthma severity was assessed retrospectively from treatment level. RESULTS: A total of 994 adults (females 64.3%) were included, of whom 90.4% currently used a preventer inhaler (71.5% pMDI). PIFR pass rates were 93.7% at R5 compared with 70.5% at R0 (P < .0001). All patients failing the R0 PIFR breathed in too fast (>60 L/min), and 20% of patients currently using pMDI failed for this reason. Independent risk factors for failing R5 were female sex, older age group, and current preventer pMDI and for failing R0 included male sex, younger age group, current preventer DPI, and mild versus severe asthma. CONCLUSIONS: This study demonstrates that most patients with asthma can achieve adequate inspiratory flow to activate high-resistance DPIs, whereas approximately a third of patients breathe in too fast to achieve recommended inspiratory flows for correct pMDI use, including one-fifth of patients who currently use a pMDI preventer.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".