The effect of three-dimensional computed tomography reconstructions on preoperative planning of tibial plateau fractures: a case–control series
Bibliographic record
Abstract
BACKGROUND: Tibial plateau fractures are a common intra-articular injury for which computed tomography (CT) scans are routinely used for preoperative planning. Three-dimensional reconstructions of CT scans have been increasingly investigated in recent years, however their role has yet to be defined. We wish to investigate the role of three-dimensional computed tomography reconstructions (3D-CT) in the preoperative planning of tibial plateau fractures. METHODS: Twelve cases of tibial plateau fractures including plain film radiographs and conventional CT scans were distributed to 21 observers (orthopaedic residents and consultants). The observers filled out a preoperative plan checklist created for this study. Three months later the same cases were distributed, in random order, this time including 3D-CT reconstructions. The same preoperative checklists were completed, and compared to the previous checklists. RESULTS: The preoperative plan checklist was able to detect differences between cases and between observers. No significant differences were detected between the total plan scores when comparing conventional CT to 3D-CT. Sub-analysis of plan specifics (incisions, hardware, adjuncts) was also not significantly different. The level of training of the observer or the fracture complexity did not affect these results. CONCLUSIONS: No significant changes were made to observer's preoperative plans with the addition of 3D-CT. 3D-CT reconstructions come at a cost to the system, and therefore their usefulness should be investigated prior to widespread use. Our study demonstrates that the addition of 3D-CT reconstructions to the preoperative workup of tibial plateau fractures did not change management plans when compared to plans made using traditional CT-scans.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".