Technological interventions in functional capacity evaluations: An insight into current applications
Bibliographic record
Abstract
BACKGROUND: Functional Capacity Evaluation (FCE) is a crucial component within return-to-work decision making. However, clinician-based physical FCE interpretation may introduce variability and biases. The rise of technological applications such as machine learning and artificial intelligence, could ensure consistent and precise results. OBJECTIVE: This review investigates the application of information and communication technologies (ICT) in physical FCEs specific for return-to-work assessments. METHODS: Adhering to the PRISMA guidelines, a search was conducted across five databases, extracting study specifics, populations, and technological tools employed, through dual independent reviews. RESULTS: Nine studies were identified that used ICT in FCEs. These technologies included electromyography, heart rate monitors, cameras, motion detectors, and specific software. Notably, although some devices are commercially available, these technologies were at a technology readiness level of 5-6 within the field of FCE. A prevailing trend was the combined use of diverse technologies rather than a single, unified solution. Moreover, the primary emphasis was on the application of technology within study protocols, rather than a direct evaluation of the technology usability and feasibility. CONCLUSION: The literature underscores limited ICT integration in FCEs. The current landscape of FCEs, marked by a high dependence on clinician observations, presents challenges regarding consistency and cost-effectiveness. There is an evident need for a standardized technological approach that introduces objective metrics to streamline the FCE process and potentially enhance its outcomes.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".