Defining ‘medical necessity’ in an age of personalised medicine: A view from Canada
Bibliographic record
Abstract
The concept of medical necessity plays a central role in many healthcare systems, including Canada's, by helping determine which healthcare services will receive funding. Despite its significance in health policy frameworks, medical necessity has proven to be notoriously difficult to define and operationalise. A shift toward a more personalised and genetically-informed approach to the provision of healthcare seems likely to heighten associated policy challenges. One of the stated goals of personalised medicine is to save healthcare systems money by facilitating the use of less and more effective treatments. However, any cost saving potential may ultimately be thwarted by physicians' legal and ethical obligations, given that physicians will inevitably be required to implement and define the bounds of genetically-informed medical necessity for their patients.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.049 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.017 | 0.034 |
| Insufficient payload (model declined to judge) | 0.003 | 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".