Toward guidelines for reporting assistive technology device outcomes
Bibliographic record
Abstract
PURPOSE: The aim of this study was to develop and pilot-test reporting guidelines for manuscripts describing studies of assistive technology device outcomes, with the hopes of improving the overall quality of research in this field. METHODS: The research is presented in two stages. In Stage 1, a literature review was completed to identify the essential components of a conceptual framework for reporting guidelines and to create a checklist. In Stage 2, two independent reviewers evaluated twenty articles using the checklist to identify any short-comings of the tool and produce an estimate of interrater reliability. Two items of the original checklist were revised after reconciling disagreements between the two raters. RESULTS: < .000), reflecting excellent interrater agreement. The overall percent agreement was 94.6%. CONCLUSIONS: Reporting guidelines for studies of assistive technology device outcomes appear to be reliable. Although the checklist may require periodic updating, it has potential for advancing outcomes research. Researchers are invited to share comments and criticisms to aid in the efforts of enhancing the quality of reporting in this field.Implications for rehabilitationReporting checklists and guidelines are effective tools for achieving a minimum standard of reporting quality in all areas of rehabilitation research.This study presents a preliminary reporting checklist for the field of assistive technology device outcomes that has potential for advancing outcomes research.Authors and journal editors are encouraged to adopt and adhere to reporting guidelines in order to enhance the clarity and completeness of prospective studies.
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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 | 0.766 | 0.854 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.035 | 0.027 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.016 | 0.015 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".