Novel tissue biomarker candidates to predict both deep venous thrombosis and healing outcome after Achilles tendon rupture
Bibliographic record
Abstract
Deep venous thrombosis (DVT) and poor long-term patient outcomes frequently occur in patients with Achilles tendon rupture (ATR). Biomarkers for DVT and their possible relationship to long-term healing outcomes remain unexplored. To identify DVT biomarkers from proteomic profiles during the inflammatory and proliferative healing stages and assess their associations with one-year healing outcomes after surgical repair of ATR. A cohort of 53 patients undergoing standardized ATR repair from previous clinical trials was investigated. Intraoperative inflammatory-stage tendon biopsies were obtained from 40 patients, and tendon microdialysates from 28 patients were collected two weeks later during the proliferative stage. Liquid chromatography-tandem mass spectrometry proteomic profiles were linked to DVT status at two weeks post-surgery using ultrasonography screening and to patient-reported outcomes at one-year post-surgery. Six candidate DVT biomarkers were identified from tendon biopsies, whereof four (ABI3BP, IGKV2-40/IGKV2D-40, PCYOX1, STIP1) were associated with one-year healing outcomes. In tendon microdialysates, 43 candidate DVT biomarkers were identified, but none were associated with healing outcomes. Bioinformatic analysis revealed pathways related to heat shock response, platelet signaling, collagen and extracellular matrix metabolism, and immunoglobulins. The results support shared inflammatory-stage protein pathways in regulating venous thrombosis and reported healing outcomes, where elements of individual hypoxic tolerance and platelet signaling emerge as potential key links.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".