The Acromegaly Treatment Satisfaction Questionnaire (Acro-TSQ): Turkish adaptation, validity, and reliability study
Bibliographic record
Abstract
Abstract Purpose The patient-reported outcome becomes important to evaluate the situation perceived by the patients and to develop new strategies. This study aims to adapt the Acromegaly Treatment Satisfaction Questionnaire (Acro-TSQ), which was specially developed for patients with acromegaly, into Turkish by conducting a validity and reliability study. Methods After the translation and back-translation process, Acro-TSQ was filled in by face-to-face interviews with 136 patients diagnosed with acromegaly and currently receiving somatostatin analogue injection therapy. Internal consistency, content validity, construct validity, and reliability of the scale were determined. Results Acro-TSQ had a six-factor structure and explained 77.2% of the total variance in the variable. The Cronbach alpha value calculated for internal reliability showed high internal consistency (Cronbach's alpha = 0.870). Factor loads of all items were found to be between 0.567 and 0.958. As a result of EFA analysis, one item fell into a different factor in the Turkish version of the Acro-TSQ, different from its original form. CFA analysis shows that acceptable fit values are obtained for fit indices. Conclusion The Acro-TSQ, a patient-reported outcome tool, shows good internal consistency, and good reliability, suggesting it is an appropriate assessment tool for patients with acromegaly in the Turkish population
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".