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Record W2692463275 · doi:10.1002/hed.24599

Efficacy of a high‐observation protocol in major head and neck cancer surgery: A prospective study

2017· article· en· W2692463275 on OpenAlexaff
Brittany Barber, Jeffrey Harris, Cameron Shillington, Shannon Rychlik, Joseph C. Dort, Michael Meier, Angela Estey, Adam Elwi, Patty Wickson, Michael S. Buss, David Zygun, Kal Ansari, Vincent L. Biron, Daniel A. O’Connell, Hadi Seikaly

Bibliographic record

VenueHead & Neck · 2017
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsAlberta Health ServicesAlberta HealthUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineHead and neck cancerLimitingIntensive care unitMechanical ventilationSedationProspective cohort studyCohortHead and neckWeaningCohort studyEmergency medicineSurgeryAnesthesiaIntensive care medicineInternal medicineRadiation therapy

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to optimize an existing clinical care pathway (CCP) for head and neck cancer with a high-observation protocol (HOP) and to determine the effect on length of intensive care unit (ICU) admission and length of stay in hospital (LOS). METHODS: The HOP mandated initiation of spontaneous breathing trials before the conclusion of the surgery, weaning of sedation, and limiting mechanical ventilation. All patients with head and neck cancer undergoing primary surgery on the HOP were compared to a historical cohort regarding length of ICU admission, ICU readmissions, and LOS. RESULTS: Ninety-six and 52 patients were observed in "historical" and "HOP" cohorts. The length of ICU admission (1.9 vs 1.2 days; p = .021), LOS (20.3 vs 14.1 days; p = .020), and ICU readmissions (10.4% vs 1.9%; p = .013) were significantly decreased in the "HOP" cohort. CONCLUSION: Rapid weaning of sedation and limiting mechanical ventilation may contribute to a shorter length of ICU admission and LOS, as well as decreased ICU readmissions. © 2017 Wiley Periodicals, Inc. Head Neck 39: 1689-1695, 2016.

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)0.005
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 score0.037
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 head0.260
GPT teacher head0.524
Teacher spread0.264 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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