Bibliographic record
Abstract
In the current climate of patient-centred or client-centred care, it is increasingly important to recognize the unique personal experience of pain. As physical therapy students in the 1970s, the authors frequently wondered why the amount of pain experienced in response to a specific injury did not appear to be uniform among patients. Why did some patients have more post-op pain than others? Why did each person behave so differently in response to pain and injury? Why did some patients develop chronic pain after a shoulder injury or become disabled by back pain, whereas others did not, even though they appeared to have a similar injury? At that time there was no “physiological explanation” for the differences in individual outcomes. In fact, we now know that one of the common misconceptions among health care professionals was that the intensity and quality of pain experienced by each person should directly reflect the type and extent of tissue injury.1 This mistaken belief often led clinicians to dichotomize the mind/body experience of pain, so that the clinical approach focused on isolating and treating tissue injury, with little effort to consider the person experiencing the pain. Individual differences in the pain experience and in observed pain behaviours were often considered—consciously or unconsciously—to be “in the patient's head.” Thankfully, pain research has grown exponentially in the last 30 years, and we now understand that pain actually is “in the brain” and that differences in each person's pain experience reflect the individual's unique nervous-system processing, based on a complex integration of genetic,2,3 biopsychomotor,4–6 and social/environmental factors. For example, recent genetic research has identified individual differences in pain tolerance and pain threshold.7 In addition, with the advent of central nervous system imaging, the roles of so-called non-physiological factors in pain processing have actually been visualized in the form of brain activity.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.013 | 0.034 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".