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Record W2046939359 · doi:10.1155/2011/472375

Comparison of <sup>111</sup>In Leakage from Labeled Endocardial and Epicardial Cells: Impact on Modeling Viability of Cells to Be Transplanted into Myocardium

2011· article· en· W2046939359 on OpenAlexafffund
Kimberley J. Blackwood, Jane Sykes, Lela Deans, Gerald Wisenberg, Frank S. Prato

Bibliographic record

VenueInternational Journal of Molecular Imaging · 2011
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsLondon Health Sciences CentreLawson Health Research InstituteWestern University
FundersCanadian Institutes of Health ResearchBoston Scientific CorporationHeart and Stroke Foundation of Canada
KeywordsAlgorithmNuclear medicineMedicineMaterials scienceAnalytical Chemistry (journal)Computer scienceChemistry

Abstract

fetched live from OpenAlex

Introduction. Previously we proposed a cellular imaging technique to determine the surviving fraction of transplanted cells in vivo. Epicardial kinetics using Indium-111 determined the Debris Impulse Response Function (DIRF) and leakage coefficient parameters. Convolution-based modeling which corrected for these signal contributions indicated that (111)In activity was quantitative of cell viability with half-lives within 20 hrs to 37 days. We determine if the 37-day upper limit remains valid for endocardial injections by comparing previous epicardial cell leakage parameter estimates to those for endocardial cells. Methods. Normal canine myocardium was injected ((111)In-tropolone) epicardially (9 injections) or endocardially (10 injections). Continuous whole body and SPECT scans for 5 hours were acquired with three weekly follow-up imaging sessions up to 20-26 days. Time-activity curves evaluated each injection type. Results. The epicardial and endocardial kinetics were not significantly different (Epi: 1286 ± 253; Endo: 1567 ± 470 hours P = .62). Conclusion. The original epicardial estimate of leakage kinetics has been validated for use in endocardial injections.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.310
Teacher spread0.289 · 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

Citations1
Published2011
Admission routes2
Has abstractyes

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