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Record W2091386152 · doi:10.4155/tde.13.147

Magnetic Therapeutic Delivery Using Navigable Agents

2014· review· en· W2091386152 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTherapeutic Delivery · 2014
Typereview
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDrug deliveryCancer therapyCancerDrugTargeted drug deliveryCancer treatmentComputer scienceNanotechnologyMedicineRisk analysis (engineering)PharmacologyMaterials scienceInternal medicine

Abstract

fetched live from OpenAlex

For treating cancer in particular, therapeutic agents have evolved in complexity in an effort to enhance targeting efficacy. So far, efforts towards the synthesis alone of new therapeutics have attracted most attention. However, present cancer treatments frequently fail because of severe side effects related to the fact that the drug accumulates in insufficient concentration at the tumor site, while being distributed over healthy tissues and organs. More recently, advanced engineering principles have been considered for the development of platforms and drug-loaded vehicles to deliver payloads to the area to be treated by navigating them using the most direct route in order to improve tumor killing effects while minimizing toxic side effects caused by drug activity in nontargeted regions. If the introduction of engineering and principles of robotics to provide complementary techniques in targeted cancer therapy prove to be beneficial, it could influence future delivery methods and the synthesis of therapeutic carriers.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.069
GPT teacher head0.325
Teacher spread0.256 · 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