Bibliographic record
Abstract
The purpose of this paper is to review the approaches for analyzing cascade impactor (CI) mass distributions produced by pulmonary drug products and the considerations necessary for selecting the appropriate analysis procedure. There are several methods available for analyzing CI data, yielding a hierarchy of information in terms of nominal, ordinal and continuous variables. Mass distributions analyzed as a nominal function of the stages and auxiliary components is the simplest approach for examining the whole mass emitted by the inhaler. However, the relationship between the mass distribution and aerodynamic diameter is not described by such data. This relationship is a critical attribute of pulmonary drug products due to the association between aerodynamic diameter and the mass of particulates deposited to the respiratory tract. Therefore, the nominal mass distribution can only be utilized to make decisions on the discrete masses collected in the CI. Mass distributions analyzed as an ordinal function of aerodynamic diameter can be obtained by introducing the stage size range, which generally vary in magnitude from one stage to another for a given type of CI, and differ between CIs of different designs. Furthermore, the mass collected by specific size ranges within the CI are often incorrectly used to estimate in vivo deposition at various regions of the respiratory tract. A CI-generated mass distribution can be directly related to aerodynamic diameter by expressing the mass collected by each size-fractionating stage in terms of either mass frequency or cumulative mass fraction less than the aerodynamic size appropriate to each stage. Analysis of the aerodynamic diameter as a continuous variable allows comparison of mass distributions obtained from different products, obtained by different CI designs, as well as providing input to in vivo particle deposition models. The lack of information about the mass fraction emitted by the inhaler that is not size-analyzed by the CI may be perceived as a disadvantage from the standpoint of comparing the total mass per actuation emitted from the inhaler mouthpiece. However, this is a limitation of the CI measurement technique rather than the data analysis procedure. Data reduction techniques can enable the large quantity of information conveyed in a mass-size distribution to be summarized in terms of representative parameters, but care needs to be exercised if utilizing model size distribution function fitting routines to avoid introducing error by the fitting procedure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.006 | 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".