Reporting of Rehabilitation Outcomes in the Traumatic Lower Limb Amputation Literature: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To synthesize the outcomes reported in the rehabilitation and community literature for adults with traumatic lower limb amputation (LLA). DATA SOURCES: The search strategy was conducted in 3 databases (Medline, EMBASE, and CINAHL) from inception to April 2022. STUDY SELECTION: To be eligible, articles could be of any design but were required to have at least 50% adult individuals with traumatic LLA and had to report on interventions and outcomes in either a rehabilitation or community setting. DATA EXTRACTION: The extracted outcomes were classified using Dodd's framework, which is designed for organizing research outcomes. Heterogeneity was observed in the outcome measures (OMs) used for evaluation. Two reviewers independently conducted the data extraction, which was verified by a third reviewer. DATA SYNTHESIS: Of the 7,834 articles screened, 47 articles reporting data on 692 individuals with traumatic LLA, met our inclusion criteria. Four core areas encompassing 355 OMs/indicators were identified: life effect (63.4%), physiological/clinical (30.1%), resource use (5.1%), and adverse events (1.4%). Physical functioning (eg, gait, mobility) was the most frequently reported outcome domain across studies, followed by nervous system outcomes (eg, pain) and psychiatric outcomes (eg, depression, anxiety). Domains such as global quality of life and role/emotional functioning were seldomly reported. CONCLUSION: The study provides a list of outcome indicators explicitly published for adults with traumatic LLA, highlighting inconsistent reporting of outcome indicators. The lack of a standardized set of OMs is a barrier to performing meta-analyses on interventions, preventing the identification of effective care models and clinical pathways. Developing a core outcome set that includes OMs relevant to the needs of the traumatic LLA population may address these issues.
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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 | 0.057 | 0.268 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.039 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".