Bibliographic record
Abstract
fort to study interindividual factors such as sex, age, and culture as they relate to pain. That is done for a number of reasons: an understanding of predispositions to pain, the features that maintain it, and suggestions for tailored treatments. The literature on sex and gender differences, for example, is quite sizeable now. Investigators have made considerable progress in considering the role of biological sex or gender identity in influencing the prevalence of pain conditions, the response to treatment, and the mechanisms used to cope with challenging pain syndromes. Typically, the majority of pain patients for many disorders is female (Berkley, 1997; LeResche, 1997; Unruh, 1996). This includes such conditions as headache, rheumatoid arthritis, fibromyalgia, irritable bowel disorder, and temporomandibular disorder. The data on prevalence have been supplemented (Fillingim, 2000; Mogil, Chesler, Wilson, Juraska, & Sternberg, 2000; Riley, Robinson, Wise, Myers, & Fillingim, 1998; Rollman & Lautenbacher, 2001) by research on biological, psychological, and sociocultural factors with the goal of understanding the underlying mechanisms, reducing the incidence of the problems, and improving the treatment of acute and chronic pain. We know, for example, that certain opioid drugs are more potent in males than in females (Craft & Bernal, 2001), that women have a moderate to large increase in sensitivity to experimentally-induced pain compared to men (Riley et al., 1998), that women are more likely than men to suffer from many forms of clinical pain C H A P T E R
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".