An economic analysis of management strategies for closed and open grade I tibial shaft fractures
Bibliographic record
Abstract
BACKGROUND: Closed and open grade I (low-energy) tibial shaft fractures are a common and costly event, and the optimal management for such injuries remains uncertain. METHODS: We explored costs associated with treatment of low-energy tibial fractures with either casting, casting with therapeutic ultrasound, or intramedullary nailing (with and without reaming) by use of a decision tree. RESULTS: From a governmental perspective, the mean associated costs were USD 3,400 for operative management by reamed intramedullary nailing, USD 5,000 for operative management by non-reamed intramedullary nailing, USD 5,000 for casting, and USD 5,300 for casting with therapeutic ultrasound. With respect to the financial burden to society, the mean associated costs were USD 12,500 for reamed intramedullary nailing, USD 13,300 for casting with therapeutic ultrasound, USD 15,600 for operative management by non-reamed intramedullary nailing, and USD 17,300 for casting alone. INTERPRETATION: Our analysis suggests that, from an economic standpoint, reamed intramedullary nailing is the treatment of choice for closed and open grade I tibial shaft fractures. Considering financial burden to society, there is preliminary evidence that treatment of low-energy tibial fractures with therapeutic ultrasound and casting may also be an economically sound intervention.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".