Pain: Understanding and challenges for the rheumatologist
Bibliographic record
Abstract
Patients who consult a rheumatologist almostuniversally report experiencing pain. Rheumatologists,in turn, use the particular characteristics of the painreport to help toward making a specific musculoskeletaldiagnosis. Although the documentation of the symptomof pain has always been an important component of therheumatologists’ assessment, the specific managementof pain may take second place to the management of theunderlying rheumatic disease process.The appreciation of pain is now an integral partof patient care and has recently been identified by theJoint Commission on Accreditation of Healthcare Or-ganizations as the fifth vital sign (1). Pain impacts theoverall well-being of patients with rheumatic disease (2).Chronic pain negatively affects the physical and psy-chological status as well as overall quality of life ofpatients, both in adults and in children (3–6). Never-theless, pain in chronic disease is often poorly man-aged (7,8).In the last decade, we have seen an extraordinaryadvance in the unraveling of pain mechanisms at themolecular level. Pain is a complex factor, since not onlyis it dependent on the underlying pathologic process, butalso is influenced by a multiplicity of factors such as thepsychological status, past pain experience, cultural back-ground, environment, and genetics of the individual. It istherefore timely to examine pain mechanisms and themanagement of pain as it pertains to rheumatologypractice.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".