The 22nd Anniversary of the Cochrane Back and Neck Group
Bibliographic record
Abstract
STUDY DESIGN: Retrospective review and literature review. OBJECTIVE: The aim of this study was to provide an update on The Cochrane Back and Neck (CBN) activities. SUMMARY OF BACKGROUND DATA: Low back pain (LBP) affects 80% of people at some time in their lives. CBN Group has been housed in Toronto at the Institute for Work & Health since 1996 and has published 85 reviews and 32 protocols in the Cochrane Library. METHODS: Narrative review of CBN publications, impact factor, usage data, and social media impact. RESULTS: In the past 3 years, CBN conducted priority setting with organizations that develop clinical practice guidelines for LBP. CBN editors and associate editors published key methodological articles in the field of back and neck pain research. The methodological quality of CBN reviews has been assessed by external groups in a variety of areas, which found that CBN reviews had higher methodological quality than non-Cochrane reviews. CBN reviews have been included in 35 clinical practice guidelines for back and neck conditions. The 2018 journal impact factor of CBN is 11.154, which is higher than the 2018 impact factor for CDSR (7.755). CBN reviews ranked 4th among 53 Cochrane review groups in terms of Cochrane Library usage data. The most accessed CBN review was "Yoga treatment for chronic non-specific low-back pain" which had 9689 full-text downloads. CBN is active on Twitter with 3958 followers. CONCLUSION: CBN has published highly utilized systematic reviews and made important methodological contributions to the field of spine research over the past 22 years within Cochrane. LEVEL OF EVIDENCE: 4.
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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 | 0.036 | 0.145 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.046 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.078 | 0.029 |
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".