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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".