TUG-ABS Português-Brasil
Bibliographic record
Abstract
Objective. To present the Brazilian version of the Timed “Up and Go” Assessment of Biomechanical Strategies (TUG-ABS-Portuguese- Brazil), a clinical tool to assess functional mobility of subjects with hemiparesis due to stroke during the performance of the Timed “Up and Go” test, and investigate its construct validity considering the perspective of Brazilian physical therapists (PTs). Method. Eleven PTs applied the TUG-ABS within their patients and answered a questionnaire related to the clinical value of the TUG-ABS (construct validity by contemporary methods). Qualitative methodology was applied to data analysis. Results. The PTs were able to point out the relevant information to clinical evaluation and decision making provided by the TUG-ABS, to describe interpretations/judgements and clinical reasoning based upon the interpretation of the results provided by the TUG-ABS and assessed as positive the score system. Conclusion. The TUG-ABS showed adequate construct validity according to the perspective of the Brazilian PTs. Furthermore, it was able to provide measures supported by the theoretical constructs in which it was based up and relevant information to the evaluation of the mobility in stroke subjects. Therefore, the TUG-ABS showed clinical utility to identify biomechanical strategies related to functional mobility of stroke subjects.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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; 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".