MétaCan
Menu
Back to cohort
Record W2127124513 · doi:10.1109/nssmic.1997.670582

Design of a microvolumetric blood counter/sampler for metabolic PET imaging in rats and mice

2002· article· en· W2127124513 on OpenAlexafffund
D. Lapointe, J. Cadorette, S. Rodrigue, D. Rouleau, Roger Lecomte

Bibliographic record

Venue1997 IEEE Nuclear Science Symposium Conference Record · 2002
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversité de Sherbrooke
FundersPolytechnique Montréal
KeywordsBlood samplingPositron emission tomographyBiomedical engineeringBlood flowNuclear medicineScintillatorArterial bloodPositronCatheterPet imagingMaterials scienceMedicinePhysicsRadiologyOpticsAnesthesiaInternal medicineDetector

Abstract

fetched live from OpenAlex

Quantitative metabolic imaging in small animals with positron emission tomography (PET) requires the determination of the tracer concentration in whole blood, arterial plasma and metabolites as a function of time. A blood counting and sampling system was designed to simultaneously measure the time-activity curve as microvolumes of blood are collected. The system consists of a flow-through counter made of a plastic scintillator to detect positrons and of a computer-controlled blood sampler based on the concept of bubble segmentation. The number and size of samples, the withdrawal speed and the sampling time are all programmable and can be modified on-line. Samples as small as 10 /spl mu/l can be repetitively obtained from an implanted arterial catheter in the femoral vein or artery of small rats (150 g) or the jugular vein of mice (20 g). For medium sampling speed (100 /spl mu/l/min) at a constant rate, the standard deviation of the sample activity is typically less than 4%. By cutting the tubing at the bubbles at the end of the experiment, samples are made available for further processing and biochemical analysis. This apparatus has become an essential tool for quantitative animal PET studies, allowing easy, reliable sampling at a low cost.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.599

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 studies0.0000.001
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 head0.040
GPT teacher head0.287
Teacher spread0.246 · 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 designBench or experimental
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

Citations0
Published2002
Admission routes2
Has abstractyes

Explore more

Same venue1997 IEEE Nuclear Science Symposium Conference RecordSame topicMedical Imaging Techniques and ApplicationsFrench-language works237,207