Can experienced surgeons predict the additional value of a CT scan in patients with displaced intra-articular distal radius fractures?
Bibliographic record
Abstract
There are no clear guidelines when an additional CT scan should be obtained for the treatment of displaced intra-articular distal radius fractures (DRF). This study aimed to investigate whether surgeons can predict the usefulness of CT scans to facilitate choice of treatment plan and/or pre-operative planning for DRF. Four surgeons evaluated 51 patients with displaced DRF. The choice of treatment (operative or nonoperative) was based on conventional radiographs. Subsequently, the surgeons were asked whether they would have requested an additional CT scan to determine this treatment choice, and also whether they required a CT scan for pre-operative planning. After 4 weeks, the additional CT scan was provided and the cases were assessed again. Based on these data, we calculated the number needed to scan (NNS) and number needed to harm (NNH) for two decision models. Model 1: Only provide a CT scan if the surgeon requested one based on their judgment of the X-rays. Model 2: CT scans for all displaced intra-articular DRF. For choice of treatment, the NNS was lower for model 1 than for model 2 (2.6 vs. 4.3) and the NNH is higher for model 1 (3.1 vs. 1.3). For pre-operative planning, the NNS (1.3 vs. 1.4) and NNH (3.7 vs. 3.4) were comparable for both models. Surgeons are able to predict the usefulness of an additional CT scan for intra-articular displaced DRF for OR indication. However, for pre-operative planning the usefulness of a CT scan is much harder to predict.
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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 | 0.005 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".