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Record W2553695491 · doi:10.1186/s40463-016-0173-z

Harmonic scalpel impact on blood loss and operating time in major head and neck surgery: A randomized clinical trial

2016· article· en· W2553695491 on OpenAlexaff
Dieter K. Fritz, T. Wayne Matthews, Shamir Chandarana, Steven C. Nakoneshny, Joseph C. Dort

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineBlood lossSurgeryHarmonic scalpelRandomized controlled trialHead and neckHead and neck surgeryOtorhinolaryngology

Abstract

fetched live from OpenAlex

BACKGROUND: Long operating time and high blood loss contribute to post-surgical morbidity. Therefore, strategies to reduce these factors should to be tested using robust methods. The purpose of this study was to evaluate the impact of using the harmonic scalpel on operating time and blood loss in patients undergoing resection for advanced oral cancer (OSCC). METHODS: Thirty-six adult head and neck cancer patients with advanced OSCC requiring primary tumor resection with uni- or bi- lateral selective neck dissection from July 2012 to September 2014 were randomized to either the control group (traditional surgery) or the experimental group (harmonic surgery). Patients older than 18 years who were able to provide informed consent were eligible. Primary outcomes of interest were: intraoperative blood loss (mL) and operative time (minutes) for the ablative part of the surgery. RESULTS: Mean blood loss in the experimental group was 260 mL versus 403 mL in the control group (p = 0.08). Mean operative time was 140 min in the experimental group and 159 min in the control group (p = 0.2). CONCLUSIONS: In this randomized controlled trial, use of the harmonic scalpel did not effect intraoperative blood loss or OR time in patients undergoing surgery for advanced OSCC. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02017834 .

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)0.009
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.034
GPT teacher head0.330
Teacher spread0.296 · 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 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

Citations15
Published2016
Admission routes1
Has abstractyes

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