Bibliographic record
Abstract
In a world where medical conditions are increasingly understood, chronic pain remains among the most difficult to diagnose and treat. Current first-line treatment of nonmalignant chronic pain include tricyclic antidepressants and physiotherapy, while topical lidocaine, nonsteroidal anti-inflammatory drugs and other antidepressants serve as appropriate second-line therapy. Opioids, though highly effective analgesics, remain medical options of last resort due to their highly addictive properties. Surgical implantation of nerve stimulators and/or spinal decompression may also be considered for treatment of chronic pain. As a parallel course of treatment, complementary and alternative medicine such as acupuncture may also be considered. Unfortunately, people with pain are among the least anticipated patients that doctors will see, and lack of both patience and expertise often result in cookie-cutter prescriptions and standardized healthcare that do not benefit individual patients. In the ever-evolving field of pain management, recent evidence has shown that a multidisciplinary approach, rather than traditional physician-based management, offers the best long-term results to patients.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.065 | 0.044 |
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".