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Record W2465777918 · doi:10.7759/cureus.670

Superseding the Hourglass Effect Toward the Successful Commercialization of Nanotechnology in the Medical Sciences – We Require a Change in Perspective

2016· article· en· W2465777918 on OpenAlexaff
Krishnan Chakravarthy, Frank Boehm, Wendy Sanhai-Madar

Bibliographic record

VenueCureus · 2016
Typearticle
Languageen
FieldMedicine
TopicScience, Research, and Medicine
Canadian institutionsLakehead University
Fundersnot available
KeywordsCommercializationNanomedicineMedicineBench to bedsideEngineering ethicsHourglassNanotechnologyPerspective (graphical)EngineeringMedical physicsComputer scienceBusinessArtificial intelligence

Abstract

fetched live from OpenAlex

Nanotechnology and, specifically, nanomedicine has been touted as the next breakthrough technology for medical sciences. Although there are large advances being seen in the preclinical phases of development, there is still a paucity of viable and effective nanomedicine technologies in the clinical setting. We attempt to provide some suggestions as to the stumbling blocks of meaningful translation of this technology from the bench to the bedside. We give due consideration to the role of evidence-based medicine, regulatory pathways, and the commercialization efforts of nanomedicine at various stages in playing key roles in moving this technology into clinical use.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.124
GPT teacher head0.423
Teacher spread0.298 · 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 designObservational
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

Citations4
Published2016
Admission routes1
Has abstractyes

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