Evaluating the physico-chemical properties of water-based and 2% lidocaine hydrochloride-based aluminum-free glass polyalkenoate cements for distal radius fixation
Bibliographic record
Abstract
Lidocaine hydrochloride is used as an anesthetic for clinical applications. This study considers the effects of the substitution of 2% lidocaine hydrochloride for deionized (DI) water on the rheological, mechanical, ion release, pH and injectable properties of two formulations of aluminum-free glass polyalkenoate cements (GPCs) using two distinct poly(acrylic) acids (PAA), E9 and E11, which have different molecular weights (Mw). The substitution of 2% lidocaine hydrochloride demonstrated increased injectability, but did not affect mechanical properties. The mechanical properties increased with time, as expected, and, in general, E9-based GPCs displayed significantly higher strengths over E11-based GPCs. With respect to ion release, which includes calcium (Ca), strontium (Sr), zinc (Zn) and silicon (Si); all ions displayed a steady and consistent increased release over time. Ca and Sr showed similar ion release patterns, whereby the GPC made with E11 PAA and lidocaine hydrochloride released significantly more ions than all other compositions likely due to similar chemical kinetics. However, Zn is also divalent in nature, but displayed only one significant difference across the GPC series at all time points, which was attributed to its higher electronegativity allowing for increased participation in the setting reaction. Finally, an analysis of the pH confirmed an increase in pH with time, suggesting that H + ions were attacking the glass structure to allow for ion release. After 1 and 7 days, water-based GPCs environments achieved a higher pH than lidocaine hydrochloride-based GPCs, indicating that the lidocaine hydrochloride may be releasing additional protons upon bond formation with PAA.
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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 | 0.001 |
| 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".