A Review of Chlorine in Indoor Swimming Pools and its Increased Risk of Adverse Health Effects
Bibliographic record
Abstract
Background: Chlorine is a commonly used agent for water disinfectant in swimming pools. Inadequate ventilation in indoor swimming pools and chlorination disinfectant by-products (DBP’s) caused by organic matter promote the increased risk of adverse health effects. Water quality and proper ventilation must be monitored to avoid health risks in youth and adolescents. Methods: Studies were researched on children and adolescents from 2-18 years old who swim indoors. Articles were limited by only including journals from the year 2000 through 2010 and contain global statistics. Peer reviewed scientific articles were reviewed and a meta-analysis of three different scientific research databases, PubMed, Web of Science and Google Scholar, was conducted. Results and Conclusions: Children under five years of age, lifeguards and elite swimmers are at an increased risk of upper and lower respiratory symptoms, such as asthma, when exposed to chlorinated swimming frequently. Recreational swimmers who swim moderately are at a lower risk for developing occupational asthma. Implications: Reducing exposure to chlorine from indoor swimming pools may limit the risk of developing upper and lower respiratory infections.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".