A Human Experimental Bone Pain Model
Bibliographic record
Abstract
The aim of this study was to develop a human experimental bone pain model. Fourteen healthy men were included in two study sessions. Pressure pain threshold (PPT) was estimated using probes of different sizes. Computer-controlled and hand-held algometry were applied to the skin area covering right and left medial tibia before and after local anaesthesia (LA) of the skin and reproducibility was evaluated. Pain experience (McGill questionnaire) was compared between healthy volunteers and 12 patients with vertebral fractures. Computer-controlled algometer: No differences in PPT between study sessions for 6 and 8-mm probes (p = 0.43 and 0.32) were seen. There was a difference in PPT before and after LA for the 6-mm probe (p = 0.008), but not for the 8-mm probe (p = 0.26). Hand-held algometer: A difference in PPT between study sessions was observed for 4- and 8-mm probes (p = 0.03 and 0.007), but not for 2, 6 and 10-mm probes (p = 0.19, 0.05 and 0.25). No differences in PPT were seen before and after LA for 2, 4, 8 and 10-mm probes (p = 0.35, 0.15, 0.08 and 0.53), but LA significantly influenced PPT with the 6-mm probe (p = 0.01). Similar words were chosen in the McGill pain questionnaire by healthy volunteers and patients, qualitatively describing the deep pain sensation. The pain evoked by hand-held algometer and the 2-mm probe was not influenced by LA, and PPT was reproducible between sessions and is recommended for studies of experimentally evoked bone-associated pain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".