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Record W3201647106 · doi:10.1007/s43939-021-00018-8

Dissolution behaviour and imageability of ternary borate glasses for use in geniculate artery embolization

2021· article· en· W3201647106 on OpenAlexafffund
R. A. Manchester, T.Z. Todorova, Elena Tonkopi, Brendan Kelly, J.P. Gosse, Christopher Davis, Kimberly Brewer, M. Shymka, Daniel Boyd

Bibliographic record

VenueDiscover Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDissolutionSuspension (topology)Ternary operationBoronMaterials scienceMicrosphereChemical engineeringChemistryComputer scienceMathematicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Sixteen borate glass compositions comprising K 2 O and SrO were screened, using a design of mixtures approach, to model compositional effects on dissolution, CT imageability, and MRI relaxivity (R 2 ). Based on the characteristics of each network, together with dose determination and toxicological risk, the composition identified as BKSA16 was selected as a preferred composition for pre-clinical evaluations related to geniculate artery embolization (GAE). Accordingly, BKSA16 particles were subjected to a flame spheroidization process and recharacterized, including the evaluation of residual mass at 72 h in physiologically representative media along with clinical determinations of suspension time (ease of use). For both the irregular particles and microspheres residual mass was present at 72 h in physiologically representative media. Additionally, both the microspheres and irregular particles achieved suspension times deemed to be acceptable for clinical use. The collective data confirms that BKSA16 microspheres have a range of beneficial features (specifically both degradable and imageable) suited to GAE.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.286
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2021
Admission routes2
Has abstractyes

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