Rationale and Design of a Randomized Controlled Trial to Evaluate the Effectiveness of Medical Student Counseling for Hospitalized Patients Addicted to Tobacco (the MS-CHAT Trial)
Bibliographic record
Abstract
Globally, India is the second largest consumer of tobacco. However, Indian medical students do not receive adequate training in smoking cessation counseling. Each patient hospitalization is an opportunity to counsel smokers. Medical Student Counseling for Hospitalized patients Addicted to Tobacco (MS-CHAT) is a 2-arm multicenter randomized controlled trial (RCT) that compares the effectiveness of a medical student-guided smoking cessation program initiated in inpatients and continued for two months after discharge versus standard hospital practice. Current smokers admitted to the hospital are randomized to receive either usual care or the intervention. The intervention group receives inpatient counseling and longitudinal postdischarge telephone follow-up by medical students. The control group receives counseling at the discretion of the treating physician. The primary outcome is biochemically verified 7-day point prevalence of smoking cessation at 6 months after enrollment. Changes in medical student knowledge and attitude will also be studied using a pre- and postquestionnaire delivered prior to and 12 months after training. This trial tests a unique model that seeks to provide hands-on experience in smoking cessation counseling to medical students while simultaneously improving cessation outcomes among hospitalized smokers in India.
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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 | 0.058 | 0.047 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".