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Record W3124450235

Hostile Takeovers and Overreliance

2015· article· en· W3124450235 on OpenAlexaff
Anthony Niblett

Bibliographic record

VenueSeattle University law review · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDamagesContext (archaeology)Breach of contractLawCompensation (psychology)Law and economicsEconomicsInvestment (military)PaymentBusinessSociologyPolitical scienceFinancePsychology
DOInot available

Abstract

fetched live from OpenAlex

Serum levels of thin filament proteins, cardiac troponin T (cTnT) and cardiac troponin I (cTnI) employing high sensitivity antibodies provide a state-of-the art determination of cardiac myocyte injury in COVID-19 patients. Although there is now sufficient evidence of the value of these determinations in patients infected with SARS-CoV-2, mechanisms of their release have not been considered in depth. We summarize the importance of these mechanisms with emphasis on their relation to prognosis, stratification, and treatment of COVID-19 patients. Apart from frank necrotic cell death, there are other mechanisms of myocyte injury leading to membrane fragility that provoke release of cTnT and cTnI. We discuss a rationale for understanding these mechanisms in COVID-19 patients with co-morbidities associated with myocyte injury such as heart failure, hypertension, arrythmias, diabetes, and inflammation. We describe how understanding these significant aspects of these mechanisms in the promotion of angiotensin signaling by SARS-CoV-2 can affect treatment options in the context of individualized therapies. Moreover, with likely omic data related to serum troponins and with the identification of elevations of serum troponins now more broadly detected employing high sensitivity antibodies, we think it is important to consider molecular mechanisms of elevations in serum troponin as an element in clinical decisions and as a critical aspect of development of new therapies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.038
GPT teacher head0.202
Teacher spread0.164 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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