An Integrated Quantitative Index for Measuring Chronic Multisite Pain: The Multiple Areas of Pain (MAP) Study
Bibliographic record
Abstract
OBJECTIVE: Despite the high prevalence of chronic multisite pain, there is little consensus on methods to characterize it. Commonly used assessments report only one dimension of pain, that is, intensity, thus ignoring the spatial aspect of pain. We developed a novel pain quantification index, the Integrated Pain Quantification Index (IPQI), on a scale of 0 to 1 that integrates multiple distinct pain measures into a single value, thus representing multidimensional pain information with a single value. DESIGN: Single-visit, noninterventional, epidemiological study. SETTING: Fourteen outpatient multidisciplinary pain management programs. PATIENTS: Patients with chronic pain of the trunk and/or limbs for at least six months with average overall pain intensity of at least 5 on the numeric rating scale. METHODS: Development of IPQI was performed in a large population (N = 810) of chronic pain patients from the Multiple Areas of Pain (MAP) study. RESULTS: Prevalence of two or more noncontiguous painful areas was at 88.3% (95% confidence interval [CI] = 0.86-0.90), with a mean of 6.3 areas (SD = 5.57 areas). Prevalence of more than 10% body area in pain was at 52.8% (95% CI = 0.49-0.56), with a mean at 16.1% (17.16%). On average, IPQI values were near the middle of the scale, with mean and median IPQI at 0.52 (SD = 0.13) and 0.55, respectively. The IPQI was generalizable and clinically relevant across all domains recommended by the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials. CONCLUSIONS: IPQI provided a single pain score for representing complex, multidimensional pain information on one scale and has implications for comparing pain populations across longitudinal clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".