Testing the low dose mixtures hypothesis from the Halifax project
Bibliographic record
Abstract
Abstract In 2013, 60 scientists, representing a larger group of 174 scientists from 26 nations, met in Halifax, Nova Scotia to consider whether – using published research – it was logical to anticipate that a mixture of chemicals, each thought to be non-carcinogenic, might act together in that mixture as a virtual carcinogen. The group identified 89 such chemicals, each one affecting one or more Hallmark(s) – collectively covering all Hallmarks of Cancer – confirming the possibility that a chemical mixture could induce all the Hallmarks and function as a virtual carcinogen, thereby supporting the concern that chemical safety research that does not evaluate mixtures, is incomplete. Based on these observations, the Halifax Project developed the Low-Dose Carcinogenesis Hypothesis which posits “…that low-dose exposures to [mixtures of] disruptive chemicals that are not individually carcinogenic may be capable of instigating and/or enabling carcinogenesis.” Although testing all possible combinations of over 80,000 chemicals of commerce would be impractical, prudence requires designing a methodology to test whether low-dose chemical mixtures might be carcinogenic. As an initial step toward testing this hypothesis, we conducted a mini review of published empirical observations of biological exposures to chemical mixtures to assess what empirical data exists on which to base future research. We reviewed studies on chemical mixtures with the criteria that the studies reported both different concentrations of chemicals and mixtures composed of different chemicals. We found a paucity of research on this important question. The majority of studies reported hormone related processes and used chemical concentrations selected to facilitate studying how mixtures behave in experiments that were often removed from clinical relevance, i.e., chemicals were not studied at human-relevant concentrations. New research programs must be envisioned to enable study of how mixtures of small doses of chemicals affect human health, starting, when at all possible, from non-malignant specimens when studies are done in vitro. This research should use human relevant concentrations of chemicals, expand research beyond the historic focus on endocrine endpoints and endocrine related cancers, and specifically seek effects that arise uniquely from exposure to chemical mixtures at human-relevant concentrations.
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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 | 0.034 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".