What Do Experimental Pain Models Tell Us about Aging and Clinical Pain?
Bibliographic record
Abstract
During presentations about aging and clinical pain, almost invariably, someone will ask, “Does pain threshold change with age? Wouldn't that explain your results?” These are complicated questions, and there is rarely time in the few minutes usually allotted for “Q and A” to give an adequate response. Instead, more than once I've replied, “What would age differences in pain threshold tell you about clinical pain?” This is not intended to challenge the audience member but rather to reflect the pain research community's long struggle to understand the relevance of data from experimental pain models to clinical pain. Most experimental, or laboratory, pain models involve the application of carefully controlled stimulation. Experimental participants are asked to indicate when they first feel the sensation (detection threshold), when it first becomes painful (pain threshold), and if stimulation is continued or its intensity increased, when they would like it to be terminated (pain tolerance) [1]. In these studies, participants are informed that they will suffer no permanent damage from the stimulation and may terminate it whenever they choose. Critics have suggested that the experimental situation cannot adequately mirror the clinical situation, especially in regard to the affective and evaluative dimensions of pain [2]. For instance, while a healthy older person may experience some transient anxiety about an upcoming shock which they have been assured will cause no permanent damage and can be easily terminated, this is far from the anxiety an older person with cancer might experience in the face of an exacerbation of pain. This person may fear disease progression, uncontrollable pain, physical disability that may necessitate institutionalization, and the impact of the pain on their quality of life and death. Proponents of experimental pain models do not deny this limitation. Instead, they point out that there are important …
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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 | 0.054 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".