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ESTIMATING RADIATION DOSE FROM TIME TO EMESIS AND LYMPHOCYTE DEPLETION

2007· article· en· W2038583103 on OpenAlexaff
Denise D. Parker, Jack Parker

Bibliographic record

VenueHealth Physics · 2007
Typearticle
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsEmergent BioSolutions (Canada)
Fundersnot available
KeywordsLymphocyteRadiation doseRadiationNuclear medicineTable (database)MedicineStatisticsImmunologyPhysicsMathematicsComputer science

Abstract

fetched live from OpenAlex

Lymphocyte depletion kinetics and time to emesis have previously been shown to correlate with radiation dose. However, the method for estimating dose from lymphocyte counts was cumbersome, and a tabulation of estimated dose vs. time to emesis published by the International Atomic Energy Agency did not agree well with a regression of data from many (>100) radiation accident cases. The time-to-emesis data have been reanalyzed, and the new regression corroborates the previously published table. Also, dose estimation from post-exposure lymphocyte counts has been simplified and no longer requires serial calculations. Instead, dose can be estimated by a simple table lookup, given the ratio of two lymphocyte counts and the time between blood samples.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.314
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations28
Published2007
Admission routes1
Has abstractyes

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