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Record W2773333037 · doi:10.1097/jom.0000000000001239

Determination of Thresholds of Radioactive Iodine Uptake Response With Clinical Exposure to Perchlorate

2017· article· en· W2773333037 on OpenAlexaff
Gretchen M. Bruce, Lisa M. Corey, Elizabeth N. Pearce, Lewis E. Braverman, Richard C. Pleus

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicChemical Analysis and Environmental Impact
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsPerchlorateConfidence intervalIodineLinear regressionIodideNuclear medicineMedicineChemistryMathematicsInternal medicineStatisticsIonInorganic chemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: To conduct a more robust examination of perchlorate exposure on iodide uptake inhibition (IUI) using pooled data from four clinical studies of perchlorate exposure. METHODS: To establish a response threshold for IUI, data were analyzed using segmented linear regression and benchmark dose (BMD) analysis. RESULTS: Segmented linear regression applied to data for 69 subjects representing nine doses identified a breakpoint corresponding to a change in the slope of the dose-response relationship of 3.0 mg/d perchlorate. The estimated BMD for a 20% decrease in iodine uptake was 2.3 mg/d, with a lower 95% confidence interval limit of 1.6 mg/d. CONCLUSIONS: A threshold dose for IUI from perchlorate exposure of 1.6 to 3.0 mg/d (0.021 to 0.038 mg/kg d) was estimated using two modeling approaches. These estimates are slightly higher than the lowest observed effect level of 0.02 mg/kg d from the Greer Study.

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.340
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2017
Admission routes1
Has abstractyes

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