A protocol for a systematic review of the effectiveness of interventions to reduce exposure to lead through consumer products and drinking water
Bibliographic record
Abstract
BACKGROUND: The toxic heavy metal lead continues to be a leading environmental risk factor, with the number of attributable deaths having doubled between 1990 and 2010. Although major sources of lead exposure, in particular lead in petrol, have been significantly reduced in recent decades, lead is still used in a wide range of processes and objects, with developing countries disproportionally affected. The objective of this systematic review is to assess the effectiveness of regulatory, environmental and educational interventions for reducing blood lead levels and associated health outcomes in children, pregnant women and the general population. METHODS/DESIGN: The databases MEDLINE, Embase and the Global Health Library (GHL) will be searched using a sensitive search strategy. Studies in English, German, French, Spanish, Italian or Afrikaans will be screened according to predefined inclusion and exclusion criteria. We will consider randomized and non-randomized studies accepted by the Cochrane Effective Practice and Organization of Care (EPOC) Group, as well as additional non-randomized studies. Screening of titles and abstracts will be performed by one author. Full texts of potentially relevant studies will be independently assessed for eligibility by two authors. A single author will extract data, with a second reviewer checking the extraction form. Risk of bias will be assessed by two researchers using the Graphical Appraisal Tool for Epidemiological studies, as modified by the Centre for Public Health at the UK National Institute for Health and Care Excellence. Any inconsistencies in the assessment of eligibility, data extraction or quality appraisal will be resolved through discussion. Where two or more studies report the primary outcome blood lead levels within the same population group, intervention category and source of lead exposure, data will be pooled using random effects meta-analysis. In parallel, harvest plots as a graphical method of evidence synthesis will be used to present findings for blood lead levels and secondary outcomes. DISCUSSION: This systematic review will fill an important evidence gap with respect to the effectiveness of interventions to reduce lead in consumer products and drinking water in the context of new WHO guidelines for the prevention and management of lead poisoning. It will also contribute to setting a future research agenda.
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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 | 0.127 | 0.171 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.016 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.113 | 0.016 |
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".