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Record W4364353417 · doi:10.1093/clinchem/hvad035

Lab Medicine in Space

2023· article· en· W4364353417 on OpenAlexafffund
Joesph R Wiencek, Saswati Das, Afshin Beheshti, Brian Crucian, Fathi Karouia, Guy Trudel, Kathleen McMonigal

Bibliographic record

VenueClinical Chemistry · 2023
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCanadian Space Agency
KeywordsLibrary scienceResearch centerMedicineComputer sciencePathology

Abstract

fetched live from OpenAlex

As humanity continues to push towards interplanetary travel and beyond, short- and long-term adaptation of the human body to extreme environmental conditions in space remains a fundamental concern. An astronaut is subjected to physically and mentally enduring situations like microgravity, extreme heat or cold, radiation, isolation, and other unforeseen technical challenges. To manage the physiological impact of these situations, the use of real-time diagnostic laboratory medicine will be necessary for the health of the astronaut. With advancing technology, the recent implementation of point-of-care testing (POCT) on the International Space Station (ISS) provides a means to reduce the need to freeze and send specimens back to earth for analysis. However, the quality of these lab tests in a microgravity environment continues to be investigated. Similar to traditional laboratory medicine, there are laboratory analysis concerns during space travel such as pre-analytical errors, competency, interface issues, specimen type differences, reference intervals, and consumable storage. As the frequency and duration of human spaceflight increases, research in these areas of laboratory testing will be key for space travel and astronaut health. For this Q&A, we invited a diverse group of experts to explore the current and desired future state of laboratory medicine in space as well as innovations that will expand our current horizon within this area of interest.

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)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.127
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.001

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 head0.065
GPT teacher head0.437
Teacher spread0.372 · 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 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

Citations3
Published2023
Admission routes2
Has abstractyes

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