Student Competition (Health Services, Economics and Policy Change) ID 1977283
Bibliographic record
Abstract
Background/Objective Assistive technologies (AT) span a large range of products from mobility aides such as canes to electronic systems that provide reminders. These technologies are important in facilitating independence, recovery and improved quality of life for individuals with spinal cord injury (SCI). However, clinical and economic outcomes for AT evaluated in the scientific literature is unclear. Thus, the aim of this study is to review the clinical and economic evidence for AT in the SCI population. Design/Methods Scientific literature databases including EMBASE, MEDLINE and CINAHL will be searched using terms identified in collaboration with a Medical Librarian. In the first stage, the titles and abstracts of clinical studies and economic analyses of AT focused in SCI will be screened by two reviewers. This will be followed by a second stage full-text screening for inclusion in the review by the same reviewers. The types of ATs evaluated will be identified along with the outcomes measured. Where applicable, study results will be presented using summary statistics. Results Work is currently underway to identify the citations. Two reviewers will then screen the titles and abstracts. It is anticipated that a large majority of the citations will be screened out with a small number of studies remaining. Most studies are also expected to be for the clinical evaluation of AT with variable outcomes. Conclusions The results of this scoping review will provide valuable insight on the types of AT where clinical and economic evidence is available and identify where the current research gaps are.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.697 | 0.247 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".