Effect of smoking on differential white cell count and hemoglobin level in healthy smokers and controls: A comparative study
Bibliographic record
Abstract
Background: Cigarette smoking alters inflammation indicators, which has been linked to cardiovascular disease as well as inflammatory disorders. The toxicity of tobacco has an effect on the oxygen saturation of haemoglobin. Total and differential leukocyte count (DLC), as well as oxygen saturation of haemoglobin, were measured in healthy smokers and nonsmokers in order to determine whether or not they were smoking. Methods: The participants in this cross-sectional study totaled 80 persons in good health. A questionnaire was utilised to gather information on smoking habits as well as anthropometric measurements such as height, weight, and body mass index (BMI). In order to count total and DLC cells in blood samples, the MS-9 automated haematological cell counter was employed. The fingertip pulse oximeter was used to test the oxygen saturation of the haemoglobin. Results: Compared to non-smokers, smokers had higher TLC (P <0.001), lymphocyte (P< 0.002), granulocyte (P 0.01), and monocyte counts (P 0.03) and lower SpO2 (P 0.03). Conclusion: The study concluded that smokers' TLC, DLC, and haemoglobin oxygen saturation should be evaluated during diagnosis, interpretation, and therapy. The elevated TLC and DLCs seen in this study may be linked to chronic inflammation and increased CVD risk in smokers. Quitting smoking is therefore beneficial to health. Keywords: SpO2; Oxygen Saturation of Hemoglobin; Total and Differential Leukocyte Count; Smokers
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".