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Value of intraoperative duplex imaging during supervised carotid endarterectomy

2001· article· en· W2088963831 on OpenAlexaff
T.S. Padayachee, Michael Brooks, C L McGuinness, K Modaresi, J. A. C. Arnold, Peter R. Taylor

Bibliographic record

VenueBritish journal of surgery · 2001
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineCarotid endarterectomyDuplex (building)RadiologyValue (mathematics)EndarterectomyStenosis

Abstract

fetched live from OpenAlex

BACKGROUND: For overall benefit, carotid endarterectomy requires low perioperative morbidity and mortality rates. Carotid thrombosis is usually secondary to technical error, which may be related to the experience of the operator. In this retrospective study the clinical and technical outcome of carotid endarterectomies performed by one consultant and five trainees were compared. METHODS: Some 149 patients underwent carotid endarterectomy; 89 were operated on by the consultant and 60 by trainees. Intraoperative duplex imaging of the carotid repair was performed before wound closure, and re-exploration was carried out when there was a residual severe stenosis associated with an intimal flap. RESULTS: There was no significant difference in clinical outcome between operations done by consultant or trainees. There was a significant increase in the number of stenoses, kinks and flaps in carotid endarterectomies performed by trainees compared with those of the consultant both before (chi2 = 12.0, 1 d.f., P < 0.001) and after (chi2 = 10.1, 1 d.f., P < 0.001) correction. CONCLUSION: Intraoperative duplex imaging may facilitate training by providing an objective assessment of the quality of the operation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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

Citations8
Published2001
Admission routes1
Has abstractyes

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