Postural control imbalance in individuals with a minor lower extremity amputation: a scoping review protocol.
Bibliographic record
Abstract
Introduction: Lower extremity amputations (LEA) impact the quality of life and physical abilities and increase the risk of developing secondary complications. While most research focuses on major LEA, minor LEA remain understudied despite their rising incidence. These amputations alter the sensorial and mechanical properties of the foot, affecting postural control and stability. Understanding these biomechanical changes is essential for improving rehabilitation strategies. Objectives: The scoping review will synthesize current research on postural control deficits following a minor LEA, focusing on any resections through or distal to the ankle joint. It will also evaluate whether interventions, such as orthotic devices and balance rehabilitation programs, have been investigated to mitigate balance impairments in this population. Inclusion criteria: The scoping review will include studies on individuals with a minor LEA, across various age, levels, and etiologies. The scoping review will focus on quantitative data related to standing balance and postural control, dynamic functional tests, and self-reported questionnaires on balance capacity and confidence. Studies assessing interventions for postural control restoration will be analyzed separately as a secondary outcome. Methods: A preliminary search of MEDLINE (PubMed) was conducted to develop a full search strategy aimed at compiling all existing scientific articles on postural control and balance in individuals with a minor LEA. The subsequent comprehensive search will be performed across multiple databases and grey literature. Two independent reviewers will independently extract the data. The Joanna Briggs Institute Quality Assessment Tool will be used to assess risk of bias and quality of included studies. Discussion: By mapping the literature on postural control in individuals with a minor LEA, the scoping review will highlight knowledge gaps and provide guidelines for future biomechanical and postural research protocols. It will also assess the current state of therapeutic intervention research as a secondary outcome, providing insights for clinical rehabilitation strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.057 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.011 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".