Serum periostin levels following small bone fractures, long bone fractures and joint replacements: an observational study
Bibliographic record
Abstract
BACKGROUND: In asthma, serum periostin may potentially be used as a biomarker in the management of patients with Type-2 eosinophilic airway inflammation. However, serum periostin may be influenced by factors other than Type 2 inflammation, potentially confounding its interpretation. We aimed to measure change in periostin following bone injury. METHODS: 102 adults without asthma were recruited into three groups: joint replacement surgery, long bone fracture, short bone fracture. Participants underwent seven measurements of serum periostin over 26 weeks after bone injury, and prior to surgery in the joint replacement group. Differences in periostin were measured using a ratio of geometric mean (RGM), with comparison made with pre-surgery (joint replacement) or 26 week (long and short fracture) reference measurements. RESULTS: In the joint replacement group, periostin fell within 48 h (RGM 0.80, 95% CI 0.75-0.86), then increased to a maximum at 8 weeks (RGM 1.89, 1.77-2.02) and by 26 weeks remained above the reference measurement (RGM 1.27, 1.19-1.36). In the long bone fracture group, periostin was reduced at 48 h (RGM 0.76, 0.71-0.83) and then progressively increased to a maximum at 8 weeks (RGM 1.15, 1.06-1.23) compared with the reference measurement. In the short bone fracture group, periostin was reduced at 48 h (RGM 0.9, 0.85-0.95) but was not different from after week 1 compared with the reference measurement. CONCLUSIONS: This trial was prospectively registered with the Australia New Zealand Trials Registry on Feb 7 2014, (ACTRN12614000151639: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=363881).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".