MétaCan
Menu
Back to cohort

Diaphragmatic dysfunction secondary to experimental lower torso ischaemia–reperfusion injury is attenuated by thermal preconditioning

2000· article· en· W2030078813 on OpenAlexfundno aff
R. McLaughlin, C. Kelly, E. Kay, D. Bouchier‐Hayes

Bibliographic record

VenueBritish journal of surgery · 2000
Typearticle
Languageen
FieldMedicine
TopicCardiac Ischemia and Reperfusion
Canadian institutionsnot available
FundersHealth Research BoardCanadian Swine Health Board
KeywordsMedicineDiaphragmatic breathingAnesthesiaTorsoIschemiaReperfusion injurySurgeryInternal medicineAnatomyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Preconditioning describes the process whereby tissue exposure to a subcritical stress confers protection from subsequent injuries. This study assessed diaphragmatic muscle function after lower torso ischaemia-reperfusion (IR) and the role of thermal preconditioning in attenuation of this injury. METHODS: Sprague-Dawley rats were randomized into three groups (24 per group): a control group, an IR group that had aortic cross-clamping for 1 h followed by reperfusion, and a third group that received thermal preconditioning 18 h before IR. Diaphragmatic function was assessed at 24 h, 48 h and 7 days. RESULTS: IR resulted in significant diaphragmatic twitch and tetanic dysfunction compared with control muscle. Thermal preconditioning significantly attenuated this injury (P < 0.05). Mean(s.e.m.) muscle twitch and tetanic forces in the IR group were 204.9(17.2) and 282.7(19.2) g respectively at 24 h. Corresponding twitch and tetanic forces in preconditioned muscle were 270.4(25.1) and 552.0(35.2) g. CONCLUSION: This study demonstrated that systemic IR injury produced a respiratory muscle mechanical dysfunction that was attenuated by thermal preconditioning, at 24 h, 48 h and 7 days. Preconditioning may have a role in clinical practice, particularly before elective surgery.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.345
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0140.000

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 head0.008
GPT teacher head0.237
Teacher spread0.229 · 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.

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

Citations10
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueBritish journal of surgerySame topicCardiac Ischemia and ReperfusionFrench-language works237,207