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The Value Of A Specimen Tracking Tool And Beyond

2018· preprint· en· W4214654833 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicSoftware Testing and Debugging Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)Tracking (education)Computer sciencePsychologyMachine learning

Abstract

fetched live from OpenAlex

Background: It is generally recognized that the integrity of tissue specimens preserved at temperatures below the gas state is stable long-term. Studies in literature have not typically extended beyond a few years. As many biobanks today may store specimens for over a decade, this assumption should be tested to provide the data to support extended long-term storage and verify that stored samples continue to be fit-for-purpose. Since its inception in 2004, the Ontario Tumour Bank (OTB) has had an ongoing commitment to quality and, in accordance with biobanking best practices, has embedded stringent quality control (QC) and quality assurance (QA) measures into its routine procedures. One such measure, triggered twice annually, includes the random selection of cryopreserved tissues to undergo quality assessment (EXTQA) to measure the integrity of the tissueu2019s DNA and RNA. As an extension of OTBu2019s routine EXTQA, we analyzed second aliquots of previously evaluated tissues collected between 2005 and 2014 to determine if RNA (presented at the ISBER annual conference in 2017) or DNA integrity (presented here) is affected by extended long-term storage in liquid nitrogen vapour phase. Methods: As previously presented, RNA was extracted from duplicate aliquots of 70 cryopreserved tissue samples across 11 disease sites and quality was determined by the RNA Integrity Number (RIN) assigned by the Agilent Bioanalyzer. As an extended fit-for-purpose assessment, DNA was extracted from 20 samples to determine if DNA Integrity is associated with time in storage. The DNA integrity of the sample, as defined by its DIN value, was determined using the Agilent 2200 Tapestation and Genomic DNA Screen Tape.Results: As presented previously, there was no significant correlation between the quality of RNA versus storage time. New to this study, there was also no significant correlation between the quality of DNA versus storage time (r=0.250, p= 0.287). As a secondary observation, DNA quality is not correlated to RNA quality (r=0.2362, p=0.3147). Conclusions: This data suggests that, as for RNA, the extended long-term storage of tumour tissue samples in vapour phase in liquid nitrogen tanks does not negatively affect the quality of DNA derivatives. As a secondary observation, RNA integrity (RIN) is not a good predictor of DNA integrity, which supports previous recommendations to consider appropriate fit-for-purpose tests for different downstream applications rather than relying solely on RIN.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.686
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.025
GPT teacher head0.278
Teacher spread0.253 · 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