Back to the Future: A Report From the 16th International Forum for Back and Neck Pain Research in Primary Care and Updated Research Agenda
Bibliographic record
Abstract
STUDY DESIGN: The 16th meeting of the International Forum for Back and Neck Pain Research in Primary Care was held in Québec City in July 2019 under the theme of innovation. This paper addresses the state of research in the field. OBJECTIVE: To ascertain the evolution of knowledge and clinical application in back and neck pain and identify shifting research priorities. MATERIALS AND METHODS: After a brief presentation of the Forum and its history, the current state of the field was depicted from the scientific program and the recordings of the plenary and parallel oral and poster communications of Forum XVI. Research agendas established in 1995 and 1997 were updated from a survey of a multidisciplinary group of experts in the field. A discussion of the progress made and challenges ahead follows. RESULTS: While much progress has been made at improving knowledge at managing back pain in the past 25 years, most research priorities from earlier decades are still pertinent. The need for integration of physical and psychological interventions represents a key challenge, as is the need to better understand the biological mechanisms underlying back and neck pain to develop more effective interventions. Stemming the tide of back and neck pain in low and middle-income countries and avoiding the adoption of low-value interventions appear particularly important. The Lancet Low Back Pain Series initiative, arising from the previous fora, and thoughts on implementing best practices were extensively discussed, recognizing the challenges to evidence-based knowledge and practice given competing interests and incentives. CONCLUSION: With the quantity and quality of research on back and neck pain increasing over the years, an update of research priorities helped to identify key issues in primary care.
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 | 0.123 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".