A case study of SMART attributes: a qualitative assessment of generalizability, retention rate, and trial quality
Bibliographic record
Abstract
BACKGROUND: Personalizing medical care is becoming increasingly popular, particularly mental health care. There is growing interest in formalizing medical decision making based on evolving patient symptoms in an evidence-based manner. To determine optimal sequencing of treatments, the sequences themselves must be studied; this may be accomplished by using a sequential multiple assignment randomized trial (SMART). It has been hypothesized that SMART studies may improve participant retention and generalizability. METHODS: We examine the hypotheses that SMART studies are more generalizable and have better retention than traditional randomized clinical trials via a case study of a SMART study of antipsychotic medications. We considered the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia study, comparing the trial participant characteristics and overall retention to those of comparable trials found via a review of all related trials conducted from 2000 onwards. RESULTS: A MEDLINE search returned 6435 results for primary screening; ultimately, 48 distinct trials were retained for analysis. The study population in CATIE was similar to, although perhaps less symptomatic than, the study populations of traditional randomized clinical trials (RCTs), suggesting no large gains in generalizability despite the pragmatic nature of the trial. However, CATIE did see good month-by-month retention. CONCLUSIONS: SMARTs offer the possibility of studying treatment sequences in a way that a series of traditional RCTs cannot. SMARTs may offer improved retention; however, this case study did not find evidence to suggest greater generalizability using this trial design. TRIAL REGISTRATION: ClinicalTrials.gov NCT00014001 . Registered on 6 April 2001.
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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 | 0.383 | 0.508 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".