Experience in Italy in the development and application of clinical guidelines for low back pain.
Bibliographic record
Abstract
UNLABELLED: Clinical Guidelines (CG) reflect the up to date scientific knowledge in the treatment of Low Back Pain (LBP). The diffusion of CG and their everyday application by health care professionals is a significant problem. As most CG are developed in English, the concerns are obviously greater in non English-speaking countries. The first CG on LBP by the Quebec Task Force (1987) was introduced in 1990 by the Gruppo di Studio della Scoliosi (GSS). Some studies where planned to verify their everyday application. The first one was carried on in Mantua, and evaluated the assessment of patients by General Practitioners (GPs): there is a clear tendency to over-prescribe examinations in acute cases, while in chronic cases under-prescription is sometimes seen. An educational approach was then proposed through a number of meetings, with fable RESULTS: A third experience verified the help GPs could receive through two different educative interventions such as a booklet and a direct access to a classical Back School. In acute patients a Booklet is useful, while Back School is not; at long term follow-up, chronic cases were significantly reduced only by the Back School approach. Finally, the Abruzzo Study's results on GPs management through computer-assisted evaluation is reported. The second part of the paper deals on the new experiences that are underway on the application of Diagnostic-Therapeutic Pathways (DTP) to Low Back Disorders.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".