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Trihalomethanes and Semen Quality in England and Wales

2009· article· en· W2016246155 on OpenAlexaff
Nina Iszatt, Mark Nieuwenhuijsen, James E. Bennett, Andrew Povey, Allan Pacey, H. D. M. Moore, Nicola Cherry, Mireille B. Toledano

Bibliographic record

VenueEpidemiology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSemen qualityConfoundingSemenMedicineEpidemiologyEnvironmental healthToxicologyEnvironmental chemistryChemistryBiologyInternal medicineAndrology

Abstract

fetched live from OpenAlex

ISEE-0663 Background and Objective: Disinfection by-products (DBPs) have been associated with adverse semen outcomes in laboratory animals. Of the DBPs, there is stronger evidence for the bromo- and chloro-acetic acids, while that for trihalomethanes (THMs) is heterogenous. However, two small epidemiological studies have found no association between DBPs and adverse semen outcomes in humans. In a large case-control study, we investigated the association between individual and total trihalomethanes (TTHM) and low motile sperm concentration (MSC) in six water regions in England and Wales between 1999 and 2002. Methods: Men were recruited from 13 fertility clinics in 9 centres across England and Wales between 1999 and 2002. THM concentrations in water zones were linked to data on semen quality for 647 cases and 936 controls, based on the men’s residence at the time the semen sample was obtained. Low MSC was calculated relative to time since last ejaculation. TTHM levels were categorized as low (< 35.71 μg/l), medium (35.71–49.86 μg/l), or high (49.87–95 μg/l). Results: Preliminary analyses using a crude measure of THM exposure (annual average THM) found no increased risk associated with TTHM. There were some excess risks found with the individual THMs. Conclusion: Our preliminary findings are inconclusive. Further analyses will use quarterly THM exposure data weighted to the time of sampling for more precise exposure, and adjust for potential confounders.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.182

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.048
GPT teacher head0.331
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2009
Admission routes1
Has abstractyes

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