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Record W2402938102 · doi:10.18192/riss-ijhs.v2i1.1526

A Review of Chlorine in Indoor Swimming Pools and its Increased Risk of Adverse Health Effects

2011· review· en· W2402938102 on OpenAlexaffvenue
Sara Angione, Heather McClenaghan, Ashley LaPlante

Bibliographic record

VenueRevue interdisciplinaire des sciences de la santé - Interdisciplinary Journal of Health Sciences · 2011
Typereview
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDisinfectantEnvironmental healthAsthmaIndoor air qualityHealth riskMedicineAdverse effectWeb of scienceToxicologyMeta-analysisEnvironmental scienceEnvironmental engineeringBiologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.834
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.412
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2011
Admission routes2
Has abstractyes

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