Pathology of Bioabsorbable Implants in Preclinical Studies
Bibliographic record
Abstract
Bioabsorbable implants can be advantageous for certain surgical tissue bioengineering applications and implant-assisted tissue repair. They offer the obvious benefits of nonpermanence and eventual restoration of the native tissue's biomechanical and immunological properties, while providing a structural scaffold for healing and a route for additional therapies (i.e., drug elution). They present unique developmental, imaging, and histopathological challenges in the conduct of preclinical animal studies and in interpretation of pathology data. The bioabsorption process is typically associated with a gradual decline (over months to years) in structural strength and integrity and may also be associated with cellular responses such as phagocytosis that may confound interpretation of efficacy and safety end points. Additionally, as these implants bioabsorb, they become increasingly difficult to isolate histologically and thus imaging modalities such as microCT become very valuable to determine the original location of the implants and to assess the remodeling response in tandem with histopathology. In this article, we will review different types of bioabsorbable implants and commonly used bioabsorbable materials; additionally, we will address some of the most common challenges and pitfalls confronting histologists and pathologists in collecting, handling, imaging, preparing tissues through histology, evaluating, and interpreting study data associated with bioabsorbable implants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| 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) | 0.004 | 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".