Utilization of MRI in surgical decision making in the shoulder
Bibliographic record
Abstract
BACKGROUND: The aim of this study is to evaluate both the utility of MRI scans and reports used in the current practice routine of shoulder surgeons and their surgical decision-making process. METHODS: Ninety-three shoulder-specialised orthopaedic surgeons of the Canadian Shoulder and Elbow Society (CSES) Orthopaedic Association were surveyed in 2020 anonymously online to help identify the use of MR-imaging and reports in managing shoulder disorders and surgical decision process. RESULTS: Thirty out of 93 (32.25%) CSES fellowship-trained orthopaedic surgeons participated. Respondents request MRI scans in about 55% of rotator cuff (RC) pathology and 48% of shoulder instability cases. Fifty percent of patients with potential RC pathology arrive with a completed MRI scan prior first orthopaedic consult. Their surgical decision is primarily based on patient history (45-55%) and physical examination (23-42%) followed by MRI scan review (2.6-18%), reading MRI reports (0-1.6%) or viewing other imaging (3-23%) depending on the shoulder disease. Ninety percent of surgeons would not decide on surgery in ambiguous cases unless the MR-images were personally reviewed. Respondents stated that shoulder MRI scans are ordered too frequently prior specialist visit as identified in more than 50% of cases depending on pathology. CONCLUSIONS: The decision-making process for shoulder surgery depends on the underlying pathology and patient history. The results demonstrate that orthopaedic surgeons are comfortable reviewing shoulder MRI scans without necessarily reading the MRI report prior to a surgical decision. MRI scans are becoming an increasingly important part of surgical management in shoulder pathologies but should not be used without assessment of patient history and or physical examination.
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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 | 0.010 | 0.078 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".