Polycaprolactone blends for fracture fixation in low load‐bearing applications
Bibliographic record
Abstract
ABSTRACT There is a need to replace surgical plates and screws in orthopedic surgery. Absorbable polymers are an alternative to metal where load bearing is of a less concern. Polycaprolactone (PCL) is biocompatible, yet it has low mechanical strength and its surface chemistry does not promote cell adhesion. The objective of this work was to create PCL adhesive blends with poly(glycolic) acid (PGA), thermoplastic starch (TPS), chitosan, and tricalcium phosphate (TCP) to be used as potential fracture fixation devices. The differential scanning calorimetry (DSC) data showed that the primary melting points (T m1 °C) of blends were often lower than PCL, with the exception of chitosan blends, which may indicate an improvement for surgical use. PCL/PGA blends showed secondary and tertiary melting points (T m) and enthalpies (ΔH m) indicating poor miscibility of PGA in the blends. The binary PCL/TCP mixture has a higher enthalpy compared to the binary PCL/PGA blend, but the secondary melting temperature is lower in ternary mixtures. Ternary blends of PCL/PGA/TCP, however, retained the adhesive strength of the parent PCL adhesive while having an improvement in hydrophilicity. These blends are recommended for fracture fixation devices especially in low load‐bearing applications such as maxillofacial surgery, orthopedics, and neurosurgery. © 2020 Wiley Periodicals Inc. J. Appl. Polym. Sci. 2020, 137, 48940.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".