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Medical Microrobots

2024· article· en· W4394681121 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

VenueAnnual Review of Biomedical Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversity of Toronto
FundersHORIZON EUROPE Framework ProgrammeEuropean Commission
KeywordsComputer scienceRisk analysis (engineering)Human–computer interactionEngineeringMedicine

Abstract

fetched live from OpenAlex

Scientists around the world have long aimed to produce miniature robots that can be controlled inside the human body to aid doctors in identifying and treating diseases. Such microrobots hold the potential to access hard-to-reach areas of the body through the natural lumina. Wireless access has the potential to overcome drawbacks of systemic therapy, as well as to enable completely new minimally invasive procedures. The aim of this review is fourfold: first, to provide a collection of valuable anatomical and physiological information on the target working environments together with engineering tools for the design of medical microrobots; second, to provide a comprehensive updated survey of the technological state of the art in relevant classes of medical microrobots; third, to analyze currently available tracking and closed-loop control strategies compatible with the in-body environment; and fourth, to explore the challenges still in place, to steer and inspire future research.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.677
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.235
Teacher spread0.231 · 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