Bibliographic record
Abstract
Like many other disciplines, medicine often resorts to metaphor in order to explain complicated concepts that are imperfectly understood. But what happens when medicine's metaphors close off thinking, restricting interpretations and opinions to those of the negative kind? This paper considers the deleterious effects of destructive metaphors that cluster around pain. First, the metaphoric basis of all knowledge is introduced. Next, a particular subset of medical metaphors in the domain of neurology (doors/keys/wires) are shown to encourage mechanistic thinking. Because schematics are often used in medical textbooks to simplify the complex, this paper traces the visual metaphors implied in such schematics. Mechanistic-metaphorical thinking results in the accumulation of vast amounts of data through experimentation, but this paper asks what the real value of the information is since patients can generally only expect modest benefits--or none at all--for relief from chronic pain conditions. Elucidation of mechanism through careful experimentation creates an illusion of vast medical knowledge that, to a significant degree, is metaphor-based. This paper argues that for pain outcomes to change, our metaphors must change first.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".