Managing Low Back Pain in the Primary Care Setting: The Know‐Do Gap
Bibliographic record
Abstract
OBJECTIVE: To ascertain knowledge gaps in the diagnosis and treatment of acute and chronic low back pain (LBP) in the primary care setting to prepare a scoping survey for identifying knowledge gaps in LBP management among Alberta's primary care practitioners, and to identify potential barriers to implementing a multidisciplinary LBP guideline. METHODS: English language studies, published from 1996 to 2008, comparing the clinical practice patterns of primary care practitioners with guideline recommendations were identified by systematically searching literature databases, the websites of various health technology assessment agencies and libraries, and the Internet. Data were synthesized qualitatively. RESULTS: The literature search identified 14 relevant studies. Knowledge gaps were reported among various primary care practitioner groups in the assessment of red flags, use of diagnostic imaging, provision of advice regarding sick leave and continuing activity, administration of some medications (muscle relaxants, oral steroids and opioids) and recommendation of particular treatments (acupuncture, physiotherapy, spinal manipulation, traction, ultrasound, transcutaneous electrical nerve stimulation and spinal mobilization). CONCLUSIONS: A know-do gap clearly exists among primary care practitioners with respect to the diagnosis and treatment of LBP. The information on know-do gaps will be used to construct a survey tool for unearthing the local knowledge gaps extant among Alberta's primary care practitioners, and to develop a dissemination strategy for a locally produced multidisciplinary LBP guideline, with the aim of ensuring that the know-do gaps inherent within each primary practice discipline are specifically targeted.
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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 | 0.024 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".