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Record W1991431055 · doi:10.3113/fai.2011.0239

Automated Cuff Occlusion Pressure Effect on Quality of Operative Fields in Foot and Ankle Surgery: A Randomized Prospective Study

2011· article· en· W1991431055 on OpenAlexaff
Alastair Younger, Mojieb Manzary, Kevin Wing, Keith Stothers

Bibliographic record

VenueFoot & Ankle International · 2011
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsVancouver Biotech (Canada)
Fundersnot available
KeywordsMedicineTourniquetAnkleCuffOcclusionRandomized controlled trialSurgeryProspective cohort studyAnesthesiaFoot and ankle surgery

Abstract

fetched live from OpenAlex

BACKGROUND: Limb occlusion pressure, which is present when blood flow ceases, has not had a practical method described for attainment. An automated tourniquet system was modified to set tourniquet pressure based on measurement of limb occlusion pressure (LOP). In this single surgeon randomized prospective study, the effectiveness of this system was assessed on patients undergoing foot and ankle surgery. MATERIALS AND METHODS: Two hundred forty-four patients were randomized to the study group of automated pressure (n = 112) or to the control group (n = 132). The primary outcome measure was tourniquet pressure used for either group. Secondary measures included the time to set the pressure and number of patients failing LOP measurement. The tourniquet field was assessed intraoperatively and postoperatively in a blinded manner. RESULTS: The tourniquet pressure was significantly lower in the study group at 198.5 ± 20.2 mmHg compared to 259.6 ± 4.4 in the control group (p < 0.001). The time to measure the LOP was 20 ± 6 seconds. Six patients failed to be measured. The quality of the surgical field was judged to be better in the study group based on all three methods of assessment. CONCLUSION: LOP measurement was a practical way of setting tourniquet pressures for limb surgery. The automated pressure averages were lower than those routinely used by most surgeons for thigh tourniquets.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.027
GPT teacher head0.331
Teacher spread0.304 · 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

Citations17
Published2011
Admission routes1
Has abstractyes

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