How to Measure Secondhand Smoke Exposure in a Pediatric Clinic Setting
Bibliographic record
Abstract
T here has been a recent focus to decrease envi- ronmental tobacco smoke (ETS) exposure among children, 1,2 but an important obstacle to overcome is how to accurately measure ETS exposure.Standardized questionnaires are associated with a high frequency of underreporting. 3,4 Cotinine, a biomarker for tobacco exposure, appears to be a more promising method to accurately detect ETS exposure, 5 but testing is currently expensive and results are not immediately available.We conducted a study to evaluate the use of a urine dipstick to measure cotinine as an alternative to the current complex testing.Methods.Patients between the ages of 5 and 15 years and their smoking or nonsmoking caregiver who attended a busy urban Pediatric Pulmonary Clinic during February and March of 2010 were invited to participate in the study.The protocol was approved by the University of South Florida institutional review board.The caregiver completed a smoking behavior questionnaire and a random urine sample was collected from the child to measure cotinine levels using Nymox TobacAlert test strips (Nymox Pharmaceutical Corporation, Saint-Laurent, Quebec, Canada).A level of 0 (0-6 ng/mL) represents no smoke exposure, levels of 1 (6-30 ng/mL) and 2 (30-100 ng/mL) represent secondhand smoke exposure, and levels of 3 to 6 (Ͼ100 ng/mL) represent a smoker.Descriptive statistics were used to summarize smoking behavior, and group comparisons between smoking households and nonsmoking households were tested by Fisher exact test or 2 test for the categorical data or by 2-sample t test for the continuous data. in our study.A larger clinical trial with measurements over serial visits is needed to support the findings of this trial.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".