Calcium polyphosphate particulates for bone void filler applications
Bibliographic record
Abstract
This study investigates the characteristics of porous calcium polyphosphate particulates (CPPp) formed using two different processing treatments as bone void fillers in non- or minimally load-bearing sites. The two calcium polyphosphate particulate variants (grades) were formed using different annealing conditions during particulate preparation to yield either more slowly degrading calcium polyphosphate particulates (SD-CPPp) or faster degrading particulates (FD-CPPp) as suggested by a previous degradation study conducted in vitro (Hu et al., Submitted for publication 2016). The two CPPp grades were compared as bone void fillers in vivo by implanting particulates in defects created in rabbit femoral condyle sites (critical size defects). The SD-CPPp and FD-CPPp were implanted for 4- and 16-week periods. The in vivo study indicated a significant difference in amount of new bone formed in the prepared sites with SD-CPPp resulting in more new bone formation compared with FD-CPPp. The lower bone formation characteristic of the FD-CPPp was attributed to its faster degradation rate and resulting higher local concentration of released polyphosphate degradation products. The study results indicate the importance of processing conditions on preparing calcium polyphosphate particulates for potential use as bone void fillers in nonload-bearing sites. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 874-884, 2017.
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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) | 0.000 | 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) | 0.001 | 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".