Hair-Normalized Cortisol Waking Response as a Novel Biomarker of Hypothalamic-Pituitary-Adrenal Axis Activity following Acute Trauma: A Proof-of-Concept Study with Pilot Results
Bibliographic record
Abstract
The mechanisms underlying the development of persistent posttraumatic pain and disability remain elusive. Recent evidence suggests that disordered stress-system pathway (hypothalamic-pituitary-adrenal axis) activity may be responsible for the genesis and maintenance of long-term sensory and emotional problems. However, confidence in current evidence is limited by the necessarily retrospective collection of data. Hair cortisol can serve as a calendar of HPA axis activity going back several months prior to injury. The purposes of this pilot study were to determine the feasibility of using hair cortisol and hair-normalized salivary cortisol as biomarkers of distress following traumatic injuries of whiplash or distal radius fracture. Ten subjects provided complete data within 3 weeks of injury. Hair cortisol, cortisol waking response (CWR), and mean daily cortisol (MDC) were captured at inception, as were self-report indicators of pain, disability, and pain catastrophizing. Pain and disability were also captured 3 months after injury. Results indicate that cortisol waking response may be a useful biomarker of current distress as measured using the pain catastrophizing scale, especially when normalized to 3-month hair cortisol (r = 0.77 raw, 0.93 normalized). Hair-normalized CWR may also have predictive capacity, correlating with 3-month self-reported disability at r = 0.70. While promising, the results must be viewed in light of the small sample.
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 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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".