Bibliographic record
Abstract
Study Design: Retrospective medical record review to assess compliance with low back pain (LBP) care indicators. Objective: To establish baseline estimates of the appropriateness of LBP care in the general Australian population provided by a range of healthcare providers in various real-world settings. Summary of Background Data: LBP is a costly condition and accounts for the greatest burden of disease worldwide, yet the care provided is often at variance with guidelines. No baseline estimates of performance are currently available in Australia across various aspects of LBP care, practitioners, and settings. Methods: A population-based sample of patients with 22 common conditions was recruited by telephone; consents were obtained to review their medical records against indicators ("CareTrack"). Care for LBP was reviewed against 10 indicators used in a previous study and ratified by experts as representing appropriate LBP care in Australia during 2009 and 2010. Results: Of the 22 CareTrack conditions, LBP had the highest number of eligible healthcare encounters (6588 of 35,573, 19%), 125 to 884 per indicator among 164 LBP patients. Overall compliance with LBP indicators was 72% (range 42%-98%). Allied health practitioners and hospitals were the most compliant (82%-83% respectively), followed by general practitioners (54%). Some aspects of care were poor, such as documenting a thorough neurological examination, screening for serious diseases such as infection and inappropriate use of drugs such as steroids and treatments such as traction. Conclusion: Over a quarter of LBP care was not appropriate despite the availability of guidelines. There is a need for national and, potentially, international agreement on clinical standards, indicators and tools to guide, document and monitor the appropriateness of care for LBP, and for measures to increase their uptake, particularly where deficiencies have been identified.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.111 | 0.027 |
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".