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Record W2097642149 · doi:10.1177/1076029613483168

Construction Noise Induces Hypercoagulability and Elevated Plasma Corticosteroids in Rats

2013· article· en· W2097642149 on OpenAlexaff
Mazen Toukh, Sheila P Gordon, Maha Othman

Bibliographic record

VenueClinical and Applied Thrombosis/Hemostasis · 2013
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsQueen's University
Fundersnot available
KeywordsThromboelastographyHemostasisMedicineCoagulationInternal medicineEndocrinologyAnesthesia

Abstract

fetched live from OpenAlex

Although animals housed for research purposes are strictly monitored for lighting, temperature, and humidity, the acoustic environment receives less attention. In a retrospective study, we investigated the effect of construction-induced noise on coagulation using thromboelastography in a group of healthy control rats. Animals were unintentionally exposed to noise due to construction in the vicinity of the animal care facility where these rats were housed. We compared the results to those of age-, gender-matched nonexposed rats. There was a significant shortening of the reaction (R) time (P = .009) and a trend toward an increase in coagulation index (CI; P = .09), indicating hypercoagulability. The short R time and increase in CI were correlated with an elevated plasma cortisol and corticosterone, indicating that the hypercoagulability seen in these rats is stress induced. Noise is a stress factor for which animals need to be monitored, particularly if those animals are selected as controls for hemostasis studies.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.345
Teacher spread0.269 · 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 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

Citations14
Published2013
Admission routes1
Has abstractyes

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