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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".