Smoking cessation in diabetic patients
Bibliographic record
Abstract
Objective Smokers with diabetes show higher risk for serious microvascular and macrovascular complications, leading them to greater mortality risk. Moreover because of insulin resistance that nicotine is causing diabetes parameters become worse in smokers. The objective was to identify the characteristics of diabetic smokers that could help increase smoking cessation rates. Methods We recruited diabetic type II, smokers (searching for diabetic you smoke in the medical files) and motivated them using WHO/ISH risk prediction charts. We assessed nicotine addiction (FNDT), metabolic rate at rest, psychometric parameters (SCL-90), Confidence, motivation, Body weight, glycosylated hemoglobin, BMI, Blood pressure, exhaled CO, CRP, Blood lipids, eating and exercise questionnaires (FFQ, IPAQ 2002). All parameters were measured at the beginning, at the end of the first month at after 3 months. The smoking cessation program is administered by a multidisciplinary team (respiratory physician, endocrinologist, dietician, behavioural psychologist) once a week for the first month and then once a month for the first trimester. All smokers are administered varenicline (free of charge) at the approved dose for 3 months. Results From the 20 first diabetic patients that were recruited, 17 (85%-13 men and 4 women) accepted following our smoking cessation program and understood that they would benefit a lot from smoking cessation. At the end of the 3m. 12 (70,5%) had quit smoking without adding weight, all of them had comorbidities (asthma, depression and coronary heart disease). The group was very addicted (Fangerstrom scale:7,8), was smoking more than one pack of cigarette (24.8cig/d) and were motivated to quit (Average motivation scale 1-10:8.6) but not confident (Average confidence scale 1-10: 5.5). These are the first results of a clinical trial of diabetic smokers (total number will be 250smokers) that will be helped to quit using intense behavioural support and varenicline for three months. Conclusions We conclude that diabetic smokers can effectively quit using an intense multidisciplinary approach, close follow-up plus varenicline for three months, without adding weight. The specific characteristics of this population will be taken into account in order to ameliorate the program. A web-based approach will be added to smokers that cannot follow the program on site. Table Average age (years) 57.6 Average cigarettes/day (before/after) 24.8/3 Average kg (before/after) 82.35/82.85 Average Fangerstrom scale before 7.8 Average motivation (scale 1 to 10) 8.6 Average confidence (scale 1 to 10) 5.5 Average SCL-90 score 77 Average BP Systolic/ Diastolic (mmHg) 124/73 Average HbA1c % 6.5 Average CO ppm (before/after) 18.6/7.17 Table I: Diabetic Type II smokers that took part at an intense smoking cessation program
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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; 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".