ESTIMATING PREVALENCE OF CARPAL TUNNEL SYNDROME AND SEVERITY USING BOSTON CARPAL TUNNEL SYNDROME QUESTIONNAIRE AMONG DEXTEROUS POPULATION
Bibliographic record
Abstract
Objective: The study was designed to estimate the prevailing percentage of Carpal Tunnel Syndrome (CTS) using Boston Carpal tunnel syndrome questionnaire (BCTSQ) in the dexterous population and to assess its severity. Study Design: This was a Cross-sectional survey. Study Settings and Participants: The study setting was Karachi where 226 Dexterous workers including; musicians, typist, dentists, butchers office workers, working for more than 1 year were recruited using non-probability convenience sampling. Outcome Measures: Boston Carpal Tunnel Syndrome Questionnaire. Results: This study enrolled 226 participants, 140 (61.9%) of which were males and 86 (38.1%) were females with mean age of 34.05±10.93. Out of the total 25 were diagnosed with CTS in which, 10 (40%) were males and 15 (60%) were females with mean age of 37.60±14.41. Hence, the prevalence of CTS among dexterous population was found to be 11.06%. Conclusion: The results of our study revealed that CTS is a prevalent neuromuscular disorder among dexterous population. The severity level varies among the population. More epidemiological studies are required to get the approximate value to promote ergonomic awareness.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".