Continuous data capture of gait and mobility metrics using wearable devices for postoperative monitoring in common elective orthopaedic procedures of the hip, knee, and spine: a scoping review
Bibliographic record
Abstract
BACKGROUND: Surgical procedures involving the hip, knee, or spine represent a majority of orthopaedic procedures performed electively in the health care system. Postoperative care is a key aspect of surgery and mobilisation without injury is the primary objective. Recent advances in wearable technologies allow objective evaluation of walking metrics to inform and guide postoperative care following orthopaedic surgery. PURPOSE: The aim of this scoping review is to explore current applications of wearable devices, objective data capture and gait analysis in monitoring postoperative recovery following commonly performed elective orthopaedic procedures of the hip, knee and spine. METHODS: A search against pre-defined criteria was performed on the following scientific databases from date of inception to February 28th, 2021: Medline (via OvidSP), Embase (via OvidSP) and Cochrane Library (via CENTRAL). Data were collected according to a predetermined checklist including study participants, surgery, wearable device (model), sensor location, and monitoring parameters such as mobility metrics, monitoring timepoints and monitoring duration for each study included in our review. Quality was assessed independently using the Newcastle Ottawa Scale (NOS). CONCLUSIONS: To our knowledge, this is the first review of wearable monitoring (of postoperative recovery) following hip, knee and spine surgery. Patients undergoing elective orthopaedic procedures may benefit from wearable monitoring of their walking health and mobility metrics.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".