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Record W2993025591 · doi:10.35219/eai1584040921

The Statistical Force in the Worldwide Performance of the Healthcare Applications, Concerning 3D Printing and the Artificial Intelligence

2019· article· en· W2993025591 on OpenAlexaboutno aff
Gabriela Opaiţ

Bibliographic record

VenueAnnals of Dunarea de Jos University of Galati Fascicle I Economics and Applied Informatics · 2019
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsnot available
Fundersnot available
Keywords3D printingLife expectancyHealth careModalities3d printedTreasureComputer scienceEngineeringBiomedical engineeringMechanical engineeringMedicineHistory

Abstract

fetched live from OpenAlex

3D Printing confers the construction of the radiant future in medicine and whole 3D Printing list concerning the healthcare material displays the energetic sources for the health people. 3D Printing reflects a huge efficiency in orthopaedy. There are impressive discoveries in this domain of the medicine: bionic hands, prosthesises, knees pans and whole 3D Printing "treasure" of health emanates energies which drive to the rise of the life expectancy. The applications for the healthcare regarding the artificial intelligence assure modalities with effectiveness through precision for to rise the healthcare at a high level. There are a lot of 3D Printing in healthcare: synthetic skin, prosthesises from titanium, cardiac models such as heart valves, hearts, cartilages for ears, electronic sensors wrapped in silicon which can be introduced in human hearts, tissues which contain blood vessels. The prosthetic appliances for patients were created at Toronto University, the tissues which include blood vessels were produced at Harvard University and the electronic sensors for human hearts were printed at Washington University.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.167

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.207
Teacher spread0.194 · 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 designSimulation or modeling
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

Citations1
Published2019
Admission routes1
Has abstractyes

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