Sacroiliac Joint: Function, Pathology, Treatment, and Contribution to Outcomes in Spine and Hip Surgery
Bibliographic record
Abstract
➢ Low back pain has a lifetime incidence of up to 84% and represents the leading cause of disability in the United States; 10% to 38% of cases can be attributed to sacroiliac joint (SIJ) dysfunction as an important pain generator.➢ Physical examination of the SIJ, including >1 provocation test (due to their moderate sensitivity and specificity) and examination of adjacent joints (hip and lumbar spine) should be routinely performed in all patients presenting with low back, gluteal, and posterior hip pain.➢ Radiographic investigations including radiographs, computed tomography, and magnetic resonance imaging with protocols optimized for the visualization of the SIJs may facilitate the diagnosis of common pathologies.➢ Intra-articular injections with anesthetic can be helpful in localizing the source of low back pain. Over-the-counter analgesics, physiotherapy, intra-articular injections, radiofrequency ablation, and surgery are all management options and should be approached from the least invasive to the most invasive to minimize the risks of complications.➢ Lumbar fusion surgery predisposes patients to more rapid SIJ degeneration and can also result in more rapid degenerative changes in the hip joints, especially with SIJ fusion.➢ Hip surgery, including hip arthroplasty and preservation surgery, is not a risk factor for SIJ degeneration, although reduced outcomes following hip surgery can be seen in patients with degenerative SIJ changes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".