MétaCan
Menu
Back to cohort
Record W2048085002 · doi:10.1289/ehp.8339

Childhood Lead Exposure in the Palestinian Authority, Israel, and Jordan: Results from the Middle Eastern Regional Cooperation Project, 1996–2000

2006· article· en· W2048085002 on OpenAlexaff
Jamal Safi, Alf Fischbein, Sameer El Haj, Ramzi Sansour, Madi Jaghabir, Mohammed Abu Hashish, Hassan D. Suleiman, Nimer Safi, Abed Abu-Hamda, Joyce K. Witt, Efim Platkov, Stephen Reingold, Amber Alayyan, T. Berman, Matti Bercovitch, Y Choudhri, Elihu D. Richter

Bibliographic record

VenueEnvironmental Health Perspectives · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsHealth Canada
Fundersnot available
KeywordsWest bankMiddle EastBlood lead levelEnvironmental healthLead poisoningGaza stripLead exposureMedicineToxicologyEnvironmental protectionGeographyPalestineAncient historyArchaeologyHistory

Abstract

fetched live from OpenAlex

In the Middle East, the major sources of lead exposure have been leaded gasoline, lead-contaminated flour from traditional stone mills, focal exposures from small battery plants and smelters, and kohl (blue color) in cosmetics. In 1998-2000, we measured blood lead (PbB) levels in children 2-6 years of age in Israel, Jordan, and the Palestinian Authority (n = 1478), using a fingerstick method. Mean (peak; percentage > 10 microg/dL) PbB levels in Israel (n = 317) , the West Bank (n = 344), Jordan (n = 382) , and Gaza (n = 435) were 3.2 microg/dL (18.2 ; 2.2%) , 4.2 microg/dL (25.7; 5.2%), 3.2 microg/dL (39.3; < 1%) , and 8.6 microg/dL (> 80.0; 17.2%), respectively. High levels in Gaza were all among children living near a battery factory. The findings, taken together with data on time trends in lead emissions and in PbB in children in previous years, indicate the benefits from phasing out of leaded gasoline but state the case for further reductions and investigation of hot spots. The project demonstrated the benefits of regional cooperation in planning and carrying out a jointly designed project.

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 score0.257
Threshold uncertainty score0.977

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 studies0.0010.001
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 head0.031
GPT teacher head0.261
Teacher spread0.229 · 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

Citations43
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Health PerspectivesSame topicHeavy Metal Exposure and ToxicityFrench-language works237,207