Utility of Specialized Imaging for Diagnosis of Chronic Wrist Pain
Bibliographic record
Abstract
Abstract Background Patients with chronic wrist pain often undergo imaging (such as magnetic resonance imaging [MRI], computed tomography [CT], or ultrasound [US]) prior to specialist assessment. Questions Is specialized wrist imaging performed prior to expert consultation necessary? Are there demographic differences between patients who do or do not receive preconsultation imaging? Patients and Methods A total of 115 patients referred to a tertiary hand center for chronic wrist pain and assessed by a hand surgeon were included. At initial consultation, surgeons were blinded to referral information and previous imaging results. The specialist performed a history, physical examination and reviewed X-rays. They established a clinical diagnosis and whether any additional investigations were needed. Prior MRI, CT, and/or US results were then reviewed and the specialists' clinical diagnosis was compared with the blinded referral diagnosis. Preconsultation imaging was categorized as having no value for diagnosis/management, some value, or high value. Results A total of 82 patients had imaging prior to specialist referral (69 MRIs, 11 CTs, and 16 ultrasounds). The majority of additional imaging (73%) was classified as unnecessary, including 77% of the MRIs and 100% of the ultrasounds. Of all the investigations performed, two CT scans were labeled highly valuable clinical aids. Older patients and those with radial-sided pain were less likely to receive preconsultation imaging. Six patients required further imaging after consultation. Conclusion Clinical assessment and X-rays are typically sufficient for a hand specialist to diagnose and manage chronic wrist pain and few patients require additional imaging. Level of Evidence This is a Level III study.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".