Nonmalignant Chronic Pain Evaluation in the Turkish Population as Measured by the McGill Pain Questionnaire
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to explore how Turkish nonmalignant pain patients described their pain and how the language of pain used by Turkish patients compares to the language found in common pain assessment tools. OBJECTIVE: Pain is influenced by a combination of ethnic, cultural, psychological, and social variants. In the Turkish language, six words are central to pain-like experiences: ağri (pain), aci (suffering), sizi (aching), sanci (colic), istirap (agony), and dert (torture). We assessed discriminant characteristics of the Turkish translation of the McGill Pain Questionnaire (MPQ). METHODS: Chronic clinical nonmalignant pain patients (n = 319, 35.7% males, 64.3% females) were questioned with the Turkish translation of the MPQ. Pain symptoms were categorized as headache (33.5%), musculoskeletal pain (33.2%), visceral pain (18.8%), and low back pain (14.5%). RESULTS: The visceral pain group had the highest mean value in the evaluative subscale (2.6 +/- 1.9). Descriptions used for sensory subscale included throbbing, sharp, aching, and tingling, while affective subscale words included tiring, suffocating, sickening, cruel, and wretched. In all pain groups, frequently chosen words for the miscellaneous subscale were nagging and penetrating. CONCLUSION: Pain descriptors were identified for each type of pain. This is, to our knowledge, the first assessment of the Turkish translation of the MPQ in nonmalignant pain patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.152 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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; both teacher heads 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".