The Evaluation of Pain in Neurosurgical Patients before and after Operation in Kerman Shahid Bahonar Hospital according to the McGill Pain Questionnaire
Bibliographic record
Abstract
The McGill Pain Questionnaire (MPQ) consists primarily of 3 major classes of word descriptors -sensory, affective and evaluative that are used by patients to specify subjective pain experience. It also contains an intensity scale and other items to determine the properties of pain experience. The questionnaire was designed to provide quantitative measures of clinical pain that can be treated statistically. This paper describes the procedures for administration of the questionnaire and the various measures that can be derived fromِ an Iranian version in comperison with previous results which were obtained from other countries. The mean and standard deviation of these measures, based on data obtained from 46 neurosurgical patients suffering from several kinds of psychocognitive and emotional status, before and after surgery in Kerman Shahid Bahonar Hospital is presented. In addition, an experimental study utilizing the questionnaire was analyzed in order to describe the nature of the information obtained by an Iranian version. The data, taken together, indicate that the McGill Pain Questionnaire provides quantitative information that can be treated statistically, and is sufficiently sensitive to detect differences among different methods of pain relieve which work with Iranian pain patients. These results show that the dimensions are close to the previous studies conducted by Melzak and Torgerson (1971) and Ebrahimi-Nejad (1997).
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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 | 0.001 | 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) | 0.001 | 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".