Evaluation of the Relationship Between Carbon Monoxide Levels and Neutrophil/Lymphocyte Ratios and Platelet/Lymphocyte Ratios in Smokers
Bibliographic record
Abstract
search, G -Funds CollectionBackground.Smoking is the most dangerous modifiable risk factor all over the world and requires the lowest cost to quit.Objectives.In this study, we aimed to determine the relationship between the Fagerström Test for Nicotine Dependence (FTND) and carbon monoxide (CO) levels and neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), monocyte/lymphocyte ratio (MLR) and RDW, WBC, MPV values.Material and methods.138 participants who applied to the Smoking Cessation Clinic of Bolu Izzet Baysal Training and Research Hospital between January 2022 and March 2022 were included in our study.After obtaining the necessary consent, the FTND test and CO levels in expiratory air were measured.The required haematological values of the participants were evaluated.Results.It was found that there was statistical significance between the CO levels of the participants and their FTND scores, and it was observed that the FTND scores increased as the CO level increased (p = 0.000).There was a statistically significant correlation between CO levels and PLR ratios and WBC levels (p = 0.024, p = 0.000).It was determined that as the CO level increased, the PLR ratios decreased, and the WBC values increased.Conclusions.In our study, a positive correlation was found between FTND levels and exhaled CO levels.We can say that CO levels can be a marker in predicting the level of addiction.In addition, it was observed that there was a significant relationship between exhaled CO levels and FTND levels, as well as PLR and WBC values.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".