The Use of Bone Scan to Investigate Back Pain in Children and Adolescents
Bibliographic record
Abstract
Children with back pain frequently undergo detailed investigation because of the perception that a high percentage will have a treatable spinal condition. The purposes of this study was (i) to determine the percentage of children with disabling back pain presenting to our institution who had a diagnosis (i.e., to explain their back pain), (ii) to evaluate the clinical markers that should alert clinicians to underlying pathology, (iii) and to determine the prognosis of children with back pain and no specific diagnosis. This study was a retrospective analysis of consecutive children undergoing single-photon emission computed tomography for a primary complaint of back pain. Data collection included chart review, radiographic analysis, and clinical follow-up with the Roland and Morris scale for pain and disability. Two hundred and seventeen patients with an average age of 13 years (range, 2.7–17.7) were reviewed on average 4.4 years after presentation (range, 1.1–7.2 years). One hundred and seventy children (78.3%) had no specific diagnosis to explain their back pain, 15 children (6.9%) had spondylosis, 10 children (4.6%) had tumor, and the remaining 22 children (10.1%) had various diagnoses including infection, Scheuermann's kyphosis, herniated disc, kidney disease, facet arthritis, degenerative disc disease, congenital anomalies, and tethered cord. Factors associated with positive diagnoses were constant pain and male gender. Night pain, constant pain, and duration of symptoms <3 months were associated with the diagnosis of a tumor. Although the majority of children presenting with persistent back pain had no demonstrable cause, of 132 contactable patients 94 (71%) had persisting pain at the time of clinical follow-up. In conclusion, the majority of children with disabling back pain has no demonstrable cause and the majority will continue to have pain years after initial presentation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".