Does Computed Tomography Change our Observation and Management of Fracture Non-Unions?
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine whether Multi-Detector Computed Tomography (MDCT) in addition to plain radiographs influences radiologists' and orthopedic surgeons' diagnosis and treatment plans for delayed unions and non-unions. METHODS: A retrospective database of 32 non-unions was reviewed by 20 observers. On a scale of 1 to 5, observers rated on X-Ray and a subsequent Multi Detector Helical Computer Tomography (MDCT) scan was performed to determine the following categories: "healed", "bridging callus present", "persistent fracture line" or "surgery advised". Interobserver reliability in each category was calculated using the Interclass Correlation Coefficient (ICC). The influence of the MDCT scan on the raters' observations was determined in each case by subtracting the two scores of both time points. RESULTS: All four categories show fair interobserver reliability when using plain radiographs. MDCT showed no improvement, the reliability was poor for the categories "bridging callus present" and "persistent fracture line", and fair for "healed" and "surgery advised". In none of the cases, MDCT led to a change of management from nonoperative to operative treatment or vice versa. For 18 out of 32 cases, the treatment plans did not alter. In seven cases MDCT led to operative treatment while on X-ray the treatment plan was undecided. CONCLUSION: In this study, the interobserver reliability of MDCT scan is not greater than conventional radiographs for determining non-union. However, a MDCT scan did lead to a more invasive approach in equivocal cases. Therefore a MDCT is only recommended for making treatment strategies in those cases.
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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 | 0.008 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".