Diagnostic imaging for spinal disorders in the elderly: a narrative review
Bibliographic record
Abstract
The high prevalence of neck and low back pain in the rapidly aging population is associated with significant increases in health care expenditure. While spinal imaging can be useful to identify less common causes of neck and back pain, overuse and misuse of imaging services has been widely reported. This narrative review aims to provide primary care providers with an overview of available imaging studies with associated potential benefits, adverse effects, and costs for the evaluation of neck and back pain disorders in the elderly population. While the prevalence of arthritis and degenerative disc disease increase with age, fracture, infection, and tumor remain uncommon. Prevalence of other conditions such as spinal stenosis and abdominal aortic aneurysm (AAA) also increase with age and demand special considerations. Radiography of the lumbar spine is not recommended for the early management of non-specific low back pain in adults under the age of 65. Aside from conventional radiography for suspected fracture or arthritis, magnetic resonance imaging (MRI) and computed tomography (CT) offer better characterization of most musculoskeletal diseases. If available, MRI is usually preferred over CT because it involves less radiation exposure and has better soft-tissue visualization. Use of subspecialty radiologists to interpret diagnostic imaging studies is recommended.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".