Weight-Bearing Monitoring Devices in Lower Extremity Fractures: A Scoping Review
Bibliographic record
Abstract
Background: Orthopaedic surgeons commonly prescribe weight-bearing parameters for their patients for a variety of reasons. Weight-bearing may be limited in order to control the healing environment, but advancing a patient's weight-bearing status is preferably done as quickly as possible to maximize functional recovery. However, it is entirely unclear to what extent these prescriptions are followed in practice. The purpose of this scoping review is to identify and compare non-invasive devices used for the measurement of weight-bearing following lower extremity fractures. Methods: Database searches of MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials (CENTRAL) were completed to identify relevant studies. Titles were screened for relevance, and abstracts were screened against the eligibility criteria. We identified studies that investigated the use of external force, pedal pressure, or activity monitoring devices used in adults after lower extremity fractures and excluded studies involving compartment pressure measurement. Findings: Sixty-two studies met the inclusion criteria. About 39% of studies used an insole-type device, which could be worn in a shoe or integrated into a removable boot. Other device types included step count or activity monitors (52%), force plates (18%), pressure film (2%) and external pedobarography systems (27%). Interpretation: We found that different monitors offered varying types of measurements and are suitable for a variety of applications. Therefore, selecting the ideal device depends on the metric of interest. Further high-quality prospective studies utilizing device monitoring are needed to validate the theory that early weight-bearing is beneficial and safe for patients with lower extremity fractures.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".