Bibliographic record
Abstract
The McGill Pain Questionnaire (MPQ), developed by Melzack (1987) , is a self-report rating scale intended to quantify the severity of pain a person may be experiencing. The main instrument is composed of 20 subcategories in which the subject is asked to describe his or her pain by choosing, from a list of several, single-word verbal pain descriptors (1 to 5) for each subcategory. The descriptors (76) encompass 4 major domains: (1) sensory, (2) affective, (3) evaluative, and (4) miscellaneous ( Melzack, 2005 ). The descriptors in each subcategory are of a hierarchical design such that they are ranked in value relative to their position in the word set. For example, the person may choose either (1) jumping, (2) flashing, or (3) shooting to describe spatial pain. A subsequent section of the measure includes an exploration of items that may exacerbate pain (20 choices) such as heat, cold, damp, or stimulants (coffee), as well as a 6-item section similar to the first to further describe the client’s pain. The total score is termed the Pain Rating Index (PRI) and ranges from 0 to 78, with higher scores associated with more pain. Scoring also provides for a unique Present Pain Index (PPI), which measures overall pain intensity drawn from six indicators ( Strand, Ljunggren, Bogen, Ask, & Johnsen, 2008 ). A short form (MPQ-SF) derived from the original is also available, which consists of only 15 descriptors of pain, 11 from the sensory and 4 from the affective categories ( Strand et al., 2008 ). The MPQ can be completed in less than 30 minutes with higher scores suggestive of more pain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.108 | 0.036 |
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".