Latent profile analysis of blood marker phenotypes and their relationships with clinical pain and interference reports in people with acute musculoskeletal trauma
Bibliographic record
Abstract
Background: The prevalence of inadequate treatments for chronic pain has necessitated the search for biological factors that influence the transition to chronicity.Methods: Antecubital blood was drawn from those who experienced acute, noncatastrophic musculoskeletal trauma. Follow-up occurred at 1, 3, 6, and 12 months with the primary outcome being Brief Pain Inventory (BPI) Functional Interference scores. Eight markers were chosen for latent profile analysis: brain-derived neurotrophic factor (BDNF); transforming growth factor-beta 1 (TGF-β1); C-reactive protein (CRP); tumor necrosis factor-alpha (TNF-α); interleukins (ILs) 1-beta, 6, and 10; and the stress hormone cortisol.Results: Mean age of the 106 participants was 44.6 years and 58.5% were female. The final model indicated a three-class solution that could be adequately described by three of the eight markers: class 1 = low concentration of all markers (33.9% of the sample), class 2 = average concentration of all markers (47.7%), and class 3 = high concentration of BDNF and TGF-β1 (18.3%). BPI Pain Interference scores captured at both inception and 6-month follow-up were compared across the three groups. Mean scores were significantly higher in class 3 for the BPI Interference subscale at inception (27.0 [SD 16.4] vs. 35.8 [SD 17.3], P = 0.05) and at 6-month follow-up (2.2 [SD 4.8] vs. 7.3 [SD 10.7], P = 0.03) compared to those of the other two classes.Conclusions: Although recovered populations are not significantly different in BDNF and TGF-β1 levels, those who experience persisting disability are more likely to have moderate to high levels in serum.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".