Proceedings of the Canadian Society for Exercise Physiology Annual General Meeting – Health and performance for the future / Santé et performance pour l’avenir
Bibliographic record
Abstract
Trikafta is a medication that targets faulty cystic-fibrosistransmembrane-conductance-regulator proteins.The present study is partnering with the Saskatchewan Health Authority (SHA) to explore overall health outcomes of initiating Trikafta in persons with cystic fibrosis (CF).Outcome measures include respiratory, cardiometabolic, fitness, neurophysiology, neuropsychology, gut health, and lifestyle factors at baseline [BL], 1mo-, 3mo-, 6mo-, 9mo-, and 12mo-post medication.The participant and research team member (female, aged 39y) has been integral in developing the research question, and has been engaged throughout the research process.Importantly, chloride sweat-test levels decreased from BL to 3mo (73mmol/L [indicative of CF] to 14mmol/L [CF unlikely]).Data collection and analyses are ongoing; BL, 1mo, and 3mo respiratory, cardiometabolic, fitness, and lifestyle factor data have been analyzed: spirometry results indicate improved lung capacity and function (FVC +8%, FEV 1 +3%, PEF +7%); cardiovascular measures improved (HR -11%, BP -9%sys/-14%dia, MAP -12%, central pulse wave velocity -19%); metabolic and body composition measures changed (fasted blood glucose -25%, resting metabolic rate -10%, WC +4%, body mass +10%, body fat +6%, body fat-free +5%); and fitness improved ( VO 2peak +8%, VEpeak +55%), as did selfreported MVPA (+210min/week).Lifestyle factor questionnaires showed decreased stress (-78%), and increased trait physical energy (300%) along with decreased trait physical (-57%) and mental (-20%) fatigue.Sleep quality improved, as did eating patterns and behaviors.The participant has selfreported reduced pain and blockage frequency [related to CF-DIOS] as well as decreased volume and frequency of sinus discharge.Other outcomes collected will be analyzed in the coming weeks.We hope to recruit further participants to explore individual variability in a case series that may inform outcomes of importance for future larger studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.121 | 0.028 |
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".