Identifying studies evaluating susceptibility factors for chemical

Overview

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Key numbers

The worker extracted the full PDF text with layout preservation twice and compared extraction hashes before commit. The following lines are copied from numeric/table-bearing regions of the PDF and retain the source units and wording where legible:

• modified when it was determined that 96% of studies found to include susceptibility information
• susceptibility. Approach 2 reduced screening by 52%, but only captured 74% of PECO-relevant
• studies when applied to the final study set. Although Approach 2 reduced screening by 12%
• Table 1) developed to focus the goals of an offshoot project of the MeHg systematic review
• average, by 60% (Howard et al., 2020). This AI/ML software tool reorders studies remaining
• Screening results are presented in Table 3 and Fig. 2. In summary, Approach 1a was used for
• screened in DistillerSR. Of these, 74 were identified as PECO relevant, and 71 or 96%
• screening of the 1998–2017b subset. In addition, because 96% of the PECO relevant studies
• dose–response studies (see Table 3) were screened for the 1998–2017b subset. The AI/ML
• 52%. During full-text screening, 97 studies were determined to be PECO relevant (See Table
• When the a priori susceptibility filter 1 (see Table 4) was applied in SWIFT Review to a
• was changed to “fatty acid*”. This resulted in susceptibility filter 4 (see Table 4). However,
• results are based on susceptibility filter 3, which identified 74% of PECO-relevant studies
• and reduced screening by 52%.
• susceptibility to chemical exposures, 96% of the studies included as PECO-relevant during
• Approach 1b), screening of epidemiology studies from all sets could be reduced by 40%,
• standard, capturing 96% of the true PECO studies was considered acceptable. Because the
• 52% fewer studies to screen, which was a greater reduction than what was achieved using
• The primary reason that the susceptibility filter failed to detect 26% of the PECO-relevant
• health#:~:text=Social%20determinants%20of%20health%20(SDOH,Education%20Access%20an
• d%20Quality.

Methods (brief)

• Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder

Implications

This page makes the source discoverable for category-level evidence routing. Values remain source-native and should be used only with the stated matrix, species, basis, geography, and censoring context from the paper. The page does not convert total mercury to methylmercury or use total arsenic as inorganic arsenic.

Wiki pages this source may touch

• Fish — marine, non-predatory (sardines, anchovies, salmon, cod)
• Seaweed/kelp foods (nori, wakame, kombu, dulse — as food products)
• Mercury
• Mercury
• Cadmium
• Lead
• Arsenic
• Chromium

Verification notes

• Identity check: DOI, raw handle, candidate cite-key, and SHA-256 were compared against existing wiki/sources/ pages before creation.
• Full-PDF read: pdftotext -layout was run on the full PDF twice; extracted text hashes matched before the page was written.
• Numeric verification: numeric/table-bearing lines were selected mechanically from the verified extraction and preserved without unit conversion or rounding.
• Brand firewall: the worker skips PDFs when extracted numeric lines appear brand/manufacturer-sensitive; this page contains category-level or species-level evidence only.
• HMTc firewall: no threshold, percentile, pass/fail, clean/dirty, or certification math is stated.

Update history

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