Additional file 2 of Pain can’t be carved at the joints: defining function-based pain profiles and their relevance to chronic disease management in healthcare delivery design
Bibliographic record
Abstract
Additional File 2: Supplementary Table 1. Overview of number of participants available for each level of analysis. Supplementary Table 2. Excluded Data Fields. Supplementary Table 3A. UKB Pain Variable Description. Supplementary Table 3B. Pain variable loading, sorted by magnitude and organized by pain profile. Supplementary Table 4. Examples of Recoding to standardize data directionality and scale. Supplementary Table 5A. Schaefer-Yeo Anatomical Labels for 7 and 17-Network Schemes. Supplementary Table 5B. Brain Variable loadings for each pain profile. For each profile, we present magnitudes of brain variable loading, sorted in descending value. Supplementary Table 6. Sex, BMI, and Age are significantly associated with all pain profiles in variable ways. Supplementary Table 7. ATC Categories for all medications included in the UKB. Supplementary Table 8. Coding scheme to translate from the UKB to the IASP nomenclature. Supplementary Table 9. Large-scale association studies reveal strong associations of all pain profiles with medications, diagnoses, and phenotypes from multiple disease categories. Supplementary Table 10. Demographic Information for the large, ~ 500,000 participant cohort and for the 34,337 participant cohort, as used to define the pain profiles.
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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 | 0.002 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.692 | 0.134 |
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".