Psychometric study of the pain drawing
Bibliographic record
Abstract
The objectives of the study were to (1) assess the extent to which interrater reliability of pain drawing location and dispersion scoring methods are similar across pain disciplines in a sample of patients with cancer treatment‐induced neuropathic pain (N = 56); and (2) investigate indicators of validity of the pain drawing in this unique sample. Patients undergoing cancer therapy completed the Brief Pain Inventory Body Map, the MD Anderson Symptom Inventory, and the McGill Pain Questionnaire. Intraclass correlation coefficients among medical and psychology professionals ranged from .93 to 99. Correlations between pain drawing score and symptom burden severity ranged from .29 to 39; correlations between pain drawing score and symptom burden interference ranged from .28 to 34. Patients who endorsed pain in the hands and feet more often described their pain as electric, numb, and shooting than patients without pain in the hands and feet. They also endorsed significantly more descriptors of neuropathic pain. Results suggest a similar understanding among members of a multidisciplinary pain team as to the location and dispersion of pain as represented by patients’ pain drawings. In addition, pain drawing scores were related to symptom burden severity and interference and descriptors of neuropathic pain in expected ways.
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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 | 0.022 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".