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Ingestible Gastrointestinal Sampling Devices: State-of-the-Art and Future Directions

2014· review· en· W2049086839 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

VenueCritical Reviews in Biomedical Engineering · 2014
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsMicropharma (Canada)McGill University
Fundersnot available
KeywordsGastrointestinal tractSampling (signal processing)Computer scienceClass (philosophy)Resource (disambiguation)Gastrointestinal systemState (computer science)DiseaseData scienceMedicineArtificial intelligencePathologyComputer visionInternal medicine

Abstract

fetched live from OpenAlex

Despite the significant contribution of gastrointestinal diseases to the global disease burden and the increasing recognition of the role played by the intestinal microbiota in human health and disease states, conventional methods of exploring and collecting samples from the gastrointestinal tract remain invasive, resource intensive, and often unable to capture all the information contained in these heterogeneous samples. A new class of gastrointestinal sampling capsules is emerging in the literature, which contains the components required for an autonomous intra-luminal device and preserves the spatial and temporal information of the gastrointestinal samples. In this paper, we identify the primary design requirements for gastrointestinal sampling capsules, and we review the state-of-the-art for different components and functionalities. We also suggest two design concepts, and we highlight future directions for this class of biomedical devices.

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)0.002
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.921
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.045
GPT teacher head0.354
Teacher spread0.309 · 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