Classification and visualization of longitudinal patterns of medication dose: An application to interferon‐beta‐1a and amitriptyline in patients with multiple sclerosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Describing patterns of use, including changes in dose and interruptions is challenging. Group-based trajectory modelling (GBTM) can be used to identify individuals with similar dose patterns. We provide an intuitive graphical representation of dose patterns in groups identified using GBTM. We illustrate our approach using two drugs with different combinations of available dosages. METHODS: We drew data on patients with MS followed from 1977 to 2014 in Montréal using two sub-cohorts of subjects. A sub-cohort of patients taking interferon-beta-1a and another of patients taking amitriptyline were identified from the initial cohort. We use GBTM to identify groups of patients with homogeneous dose patterns for each of the two drugs. We compared the graphical representation obtained from the fitted values of GBTM with our proposed approach, which consisted of using step functions whose values corresponded to the mode. Differences in characteristics across groups were identified using chi-squares and analysis of variance, both weighted by the posterior probability of group membership. RESULTS: Seven patterns of dose were identified for interferon-beta-1a and five for amitriptyline. The graphical representations of the patterns of dose from GBTM included values outside of the prescribed doses and did not capture changes in dose as clearly as the proposed representation using step functions. CONCLUSION: Our proposed approach which is based on the mode at each visit in each pattern provides an intuitive and realistic representation of dose patterns in groups identified with GBTM.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it