Multiple chemical sensitivity: It's time to catch up to the science
Bibliographic record
Abstract
Multiple chemical sensitivity (MCS) is a complex medical condition associated with low dose chemical exposures. MCS is characterized by diverse features and common comorbidities, including fibromyalgia, cough hypersensitivity, asthma, and migraine, and stress/anxiety, with which the syndrome shares numerous neurobiological processes and altered functioning within diverse brain regions. Predictive factors linked to MCS comprise genetic influences, gene-environment interactions, oxidative stress, systemic inflammation, cell dysfunction, and psychosocial influences. The development of MCS may be attributed to the sensitization of transient receptor potential (TRP) receptors, notably TRPV1 and TRPA1. Capsaicin inhalation challenge studies demonstrated that TRPV1 sensitization is manifested in MCS, and functional brain imaging studies revealed that TRPV1 and TRPA1 agonists promote brain-region specific neuronal variations. Unfortunately, MCS has often been inappropriately viewed as stemming exclusively from psychological disturbances, which has fostered patients being stigmatized and ostracized, and often being denied accommodation for their disability. Evidence-based education is essential to provide appropriate support and advocacy. Greater recognition of receptor-mediated biological mechanisms should be incorporated in laws, and regulation of environmental exposures.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".