Effects of Passive Smoking on Odour Identification in Children
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The effect of passive smoking on odour identification in children has rarely been reported. This study assessed the ability of such young subjects to identify a variety of odours. METHODS: The study population consisted of 20 children, 10 who were exposed to passive smoke at home and 10 with nonsmoking parents. Ten odourants were tested: vinegar, ammonia, peppermint, roses, bleach, vanilla, cough drops, turpentine, licorice, and mothballs. Each child was presented with five test trays containing all 10 odourants in random order. RESULTS: Of the total of 500 odours presented, the control group correctly identified 396 (79%) and the study group identified 356 (71%) (p < .005). The study group tended to misidentify 4 of the 10 odourants tested, namely, vanilla, roses, mothballs, and cough drops-56 of 200 (28%), compared with 96 of 200 (48%) in the control group. This was a highly significant finding (p < .0005). CONCLUSION: This work demonstrated that children exposed to passive smoke have difficulty identifying odours in comparison with children raised in relatively smoke-free environments. The identification of four odourants, vanilla, roses, mothballs, and cough drops, was particularly diminished in this study group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".