Bibliographic record
Abstract
OBJECTIVE: The etiology of multiple chemical sensitivity (MCS) is still debated, which is an obstacle to assessing treatment options. An analysis of the scientific literature combined with the clinical experience can suggest some avenues. METHODS: The etiology of MCS and its underlying mechanisms were reviewed from the scientific literature to identify the main factors contributing to its development. The results of the studies involving biomarkers and cerebral imaging techniques on MCS subjects were compared with those performed on subjects having the comorbidities of MCS. From the scientific literature and the experience in a clinical setting in occupational and environmental medicine, distinct types of MCS were looked for, with the application of the underlying mechanisms. The potential effectiveness of available treatments was also reviewed. RESULTS: Among many factors, unresolved emotional traumas causing chronic and acute stress reactions play an important role in the development of MCS and can be the basis for effective treatment. We identified three types of clinical presentations, called the accidental type, following a toxic exposure causing an associated emotional trauma, the associative type, following a repeated innocuous exposure in a threatening context, and the developmental type, following a traumatic childhood/adolescence causing hypervigilance and chronic stress/trauma-related disorders. We presented real cases to illustrate these types and the mechanisms behind their development, as well as effective resolution. CONCLUSIONS: MCS and its comorbidities could be treated effectively when the underlying emotional trauma(s) are targeted using trauma-focused psychotherapy and other therapies. Diagnostic criteria, principles of treatment and prevention, and avenues for research were derived from this analysis.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".