Identifying and managing adverse environmental health effects: 1. Taking an exposure history.
Bibliographic record
Abstract
Public concern and awareness are growing about adverse health effects of exposure to environmental contaminants. Frequently patients present to their physicians with questions or concerns about exposures to such substances as lead, air pollutants and pesticides. Most primary care physicians lack training in and knowledge of the clinical recognition, management and avoidance of such exposures. We have found that it can be helpful to use the CH2OPD2 mnemonic (Community, Home, Hobbies, Occupation, Personal habits, Diet and Drugs) as a tool to identify a patient's history of exposures to potentially toxic environmental contaminants. In this article we discuss why it is important to take a patient's environmental exposure history, when and how to take the history, and how to interpret the findings. Possible routes of exposure and common sources of potentially toxic biological, physical and chemical substances are identified. A case of sick-building syndrome is used to illustrate the use of the mnemonic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".