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Record W2038429562 · doi:10.1002/jbm.b.30550

Real‐time monitoring of the setting reaction of brushite‐forming cement using isothermal differential scanning calorimetry

2006· article· en· W2038429562 on OpenAlexaff
Michael Hofmann, Showan N. Nazhat, Uwe Gbureck, Jake E. Barralet

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2006
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsDifferential scanning calorimetryBrushiteCementMaterials scienceIsothermal processIndentationCalorimetryBiomedical engineeringAnalytical Chemistry (journal)Composite materialChemical engineeringCalciumThermodynamicsChemistryChromatographyMetallurgyMedicine

Abstract

fetched live from OpenAlex

The setting behavior of a brushite-forming cement (beta-tricalcium phosphate/mono calcium monophosphate) was investigated using an indentation technique (the Gillmore needles method) and isothermal differential scanning calorimetry (DSC). The two objectives of the study were to investigate whether DSC could be used to real-time monitor a fast-setting calcium phosphate cement (CPC) and to determine if it is possible to correlate DSC results directly with conventional setting-time measurements. Best-fit linear correlation analysis revealed that both the initial and final setting time (T(i) and T(f)) measured by indentation were strongly correlated to the maximum heat flow measured with DSC. It seems therefore possible to predict the setting times, usually achieved with user dependent indentation methods, of this specific fast setting CPC on the basis of objective DSC measurements. The drawbacks of DSC, however, are its overall complexity and expense and the fact that only exothermal reactions can be investigated in comparison to the Gillmore needles method, furthermore, it is not possible to monitor the complete reaction as the first 2 or 3 min are lost due to sample preparation and apparatus set up.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.296
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2006
Admission routes1
Has abstractyes

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