Just what the Doctor Ordered: A Canadian Approach to Medical Monitoring and Toxic Risk
Bibliographic record
Abstract
This article asks whether a pre-manifestation cause of action for medical monitoring might assist victims of mass toxic exposure in their ongoing struggle to recover damages for personal injuries. Medical monitoring claims normally include requests for the periodic application of a diagnostic medical examination or test designed to detect a latent disease, but whose cost is not fully covered by a provincial or private health insurer. It is argued that to require a present physical injury before such costs are awarded is completely inconsistent with the underlying preventive purpose of medical monitoring and the common law’s jealous protection of personal autonomy over the body. Instead, a prima facie claim for medical monitoring should arise whenever a defendant tortiously exposes a plaintiff to a chemical or toxic substance that increases her risk of developing a serious latent disease for which medical monitoring is reasonably necessary. Such a defendant could avoid liability by relying on any of the usual tort law defences, or by challenging the appropriateness of the requested monitoring regime. Class members would also be required to prove exposure and their collective need for a particular screening test to minimize the risk of indeterminate liability. Within these parameters, medical monitoring can properly address toxic risk, deter the irresponsible discharge of synthetic chemicals, and create a system of civil liability where recovery for involuntary toxic exposures is truly possible.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".