A Breath of Fresh Air: Abstracts from the 1st Canadian Respiratory Conference
Bibliographic record
Abstract
Both a high BMI and poor medication adherence are examples of poor selfregulatory behaviours.Both have been shown to reduce asthma control, though little is known about the extent to which they interact to worsen asthma control.This study assessed the interaction of BMI and medication adherence on asthma control in 359 adult patients (58% women, mean age = 47 years).All patients underwent a sociodemographic and medical interview (including self-reported inhaled corticosteroid [ICS] adherence, height and weight), completed the Asthma Control Questionnaire, and underwent pulmonary function testing on the day of their asthma clinic visit.BMI was categorized as normal, overweight, and obese using standard definitions.Adherence was categorized as taking ICS medications as prescribed (73%) vs. not as prescribed (i.e., never, more and less than prescribed: 27%).For patients reporting taking their ICS medication as prescribed, they estimated the % of time they took them as prescribed (mean = 96.7%).General linear models (GLM) revealed a main effect of the binary adherence variable (F=12.3,p<.001), a trend for BMI (F=2.63,p=.074) and no interaction effect (F=0.55, p=.577), such that patients who did not take their ICS medications as prescribed had worse control and patients with higher BMI's tended to have worse control.A second GLM revealed a main effect of BMI (F=2.38,p=.018) but no main effect of % of time ICS medications were taken as prescribed (F=0.19,p=.853), however, there was a significant interaction (F=-2.31,p=.022) such that worse control was associated with increasing BMI in those who did not take their ICS medications as prescribed.All analyses controlled for age, sex, and asthma severity.In spite of the limitations in the measure of adherence used, there was an interaction between adherence and BMI, indicating that both of these variables should be targeted to improve asthma control.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.146 | 0.038 |
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".