Adaptation and validation of the Iranian version of Angina Pectoris characteristics questionnaire
Bibliographic record
Abstract
Research aimed at evaluating Angina Pectoris (AP) symptoms in patients with Coronary Heart Disease (CHD) requires a comprehensive assessment tool applicable to this population. Because no single existing measure assessed the full range of symptom characteristics important to our study, we combined two measures (the modified Rose Questionnaire (MRQ), and the Short Form-McGill Pain Questionnaire (SF-MPQ) and added one item to examine specific aspects of AP symptoms (Accompanying Symptom Checklist). The purposes of this study are: (i) to describe the process of translating the SF-MPQ, MRQ and accompanying symptom checklist from English to the Farsi language; and (ii) to evaluate substantial psychometric works (content and construct validity) for the entire instrument and each part of it (SF-MPQ, MRQ and accompanying symptom checklist). A cross-cultural adaptation of the instrument was carried out before developing the final version of it. To these ends, one hundred and 10 CHD patients with AP were assessed by this hybrid measure in an initial study. An analysis of the results indicated that a content valid instrument had been developed. There was a significantly high correlation between scores in the SF-MPQ and there were associations between items in the MRQ. The Iranian version of AP characteristics questionnaire is valid; it is capable of describing characteristics of AP symptoms. However, the reliability of the instrument needs to be assessed through further research.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".