Knowledge to Action: A Challenge for Neck Pain Treatment
Bibliographic record
Abstract
SYNOPSIS: For clinicians, systematic reviews can enhance incorporation into practice of the large volumes of information emerging from research on effectiveness and risks. But we believe that these reviews are most useful with simplified tools to facilitate translation of this knowledge into practice. We provide a "Neck Care Tool Kit" that gives a diagrammatic approach to prioritizing intervention. The evidence from a series of 11 systematic reviews by the Cervical Overview Group is depicted in decision flow-charts and tables to enhance clinical interpretation of the overview findings. On simple visual inspection of symbols in a table, the reader can establish where there is evidence of benefit or no benefit, the strength of the recommendation, and if these data represent short- or long-term findings. Where possible, we guide clinicians to dosage of specific treatment methods. There is no consensus as to which outcome measures to prioritize among the large number in use. This clinical commentary guides clinicians to view the evidence in enough detail to integrate it into their clinical practice environment. We conclude by delineating research gaps and proposing future research directions. LEVEL OF EVIDENCE: Therapy, level 5.
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 | 0.109 | 0.343 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.015 | 0.029 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".