Methods for the Best Evidence Synthesis on Neck Pain and Its Associated Disorders
Bibliographic record
Abstract
STUDY DESIGN: Best evidence synthesis. OBJECTIVE: To provide a detailed description of the methods undertaken in a systematic search and perform a best evidence synthesis on the frequency, determinants, assessment, interventions, course and prognosis of neck pain, and its associated disorders. SUMMARY OF BACKGROUND DATA: Neck pain is an important cause of health burden; however, the published information is vast, and stakeholders would benefit from a summary of the best evidence. METHODS: The Bone and Joint Decade 2000-2010 Task Force on Neck Pain and its Associated Disorders conducted a systematic search and critical review of the literature published between 1980 and 2006 to assemble the best evidence on neck pain. Citations were screened for relevance to the Neck Pain Task Force mandate, using a priori criteria, and relevant studies were critically reviewed for their internal scientific validity. Findings from studies meeting criteria for scientific validity were synthesized into a best evidence synthesis. RESULTS: We found 31,878 citations, of which 1203 were relevant to the mandate of the Neck Pain Task Force. After critical review, 552 studies (46%) were judged scientifically admissible and were compiled into the best evidence synthesis. CONCLUSION: The Bone and Joint Decade 2000-2010 Task Force on Neck Pain and its Associated Disorders undertook a best evidence synthesis to establish a baseline of the current best evidence on the epidemiology, assessment and classification of neck pain, as well as interventions and prognosis for this symptom. This article reports the methods used and the outcomes from the review. We found that 46% of the research literature was of acceptable scientific quality to inform clinical practice, policy-making, and future research.
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 | 0.345 | 0.603 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.025 | 0.034 |
| Bibliometrics | 0.060 | 0.046 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.063 | 0.010 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".