Effect of increased MRI and CT scan utilization on clinical decision-making in patients referred to a surgical clinic for back pain
Bibliographic record
Abstract
BACKGROUND: We sought to determine the association between radiologic and clinical diagnoses and to measure the impact of more magnetic resonance imaging (MRI) and computed tomography (CT) scans on clinical decision-making in patients referred to a surgical clinic for back pain. METHODS: We conducted a 7-week prospective study of patients referred for back pain to spine surgeons in 1 health care centre. Patients were included if they had not previously been seen by a surgeon for their back problems and if their back pain was related to the thoracic or lumbar spine. We collected demographic data, imaging findings, clinical diagnoses as determined by the surgeons and visit outcomes and compared our results with those of a similar study conducted in 1996. RESULTS: Of 160 patients, 8 (5%) were no-shows and excluded from further analysis owing to incomplete data. There were more MRI scans and fewer plain radiographs ordered in 2009 compared with 1996 (73% v. 11% and 39% v. 68%, respectively). Degenerative disc disease was a more common radiologic diagnosis (n=78, 63%) than clinical diagnosis (n=41, 27%). Disc herniation was a more common radiologic diagnosis (n=69, 56%) than clinical diagnosis (n=25, 16%). With regards to visit outcomes, there were fewer second opinions sought in 2009 compared with 1996 (3% v. 11%). Although not statistically significant, the number of surgical candidates remained relatively stable (19% in 1996 v. 16% in 2009, p=0.44). CONCLUSION: The clinical diagnosis had a poor association with radiologic abnormalities. Despite an increase in the number of MRI and CT scans, the number of patients deemed surgical candidates has not changed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".