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Record W4384819979 · doi:10.1001/jamaoto.2023.1638

Identifying Factors of Operative Efficiency in Head and Neck Free Flap Reconstruction

2023· article· en· W4384819979 on OpenAlexaffabout
Muayyad Alhefzi, Jennifer Redwood, Alexandra Hatchell, J. L. Matthews, W. K. Fraser Hill, D. B. McKenzie, Shamir Chandarana, T. Wayne Matthews, Robert D. Hart, Joseph C. Dort, Christiaan Schrag

Bibliographic record

VenueJAMA Otolaryngology–Head & Neck Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicinePsychological interventionSurgeryModalitiesFree flap reconstructionMultidisciplinary teamCohortMicrosurgeryHead and neckAnastomosisHead and neck cancerGeneral surgeryRadiation therapy

Abstract

fetched live from OpenAlex

Importance: Head and neck oncological resection and reconstruction is a complex process that requires multidisciplinary collaboration and prolonged operative time. Numerous factors are associated with operative time, including a surgeon's experience, team familiarity, and the use of new technologies. It is paramount to evaluate the contribution of these factors and modalities on operative time to facilitate broad adoption of the most effective modalities and reduce complications associated with prolonged operative time. Objective: To examine the association of head and neck cancer resection and reconstruction interventions with operative time. Design, Setting, and Participants: This large cohort study included all patients who underwent head and neck oncologic resection and free flap-based reconstruction in Calgary (Alberta, Canada) between January 1, 2007, and March 31, 2020. Data were analyzed between November 2021 and May2022. Interventions: The interventions that were implemented in the program were classified into team-based strategies and the introduction of new technology. Team-based strategies included introducing a standardized operative team, treatment centralization in a single institution, and introducing a microsurgery fellowship program. New technologies included use of venous coupler anastomosis and virtual surgical planning. Main Outcomes and Measures: The primary outcome was mean operative time difference before and after the implementation of each modality. Secondary outcomes included returns to the operating room within 30 days, reasons for reoperation, returns to the emergency department or readmissions to hospital within 30 days, and 2-year and 5-year disease-specific survival. Multivariate regression analyses were performed to examine the association of each modality with operative time. Results: A total of 578 patients (179 women [30.9%]; mean [SD] age, 60.8 [12.9] years) undergoing 590 procedures met inclusion criteria. During the study period, operative time progressively decreased and reached a 32% reduction during the final years of the study. A significant reduction was observed in mean operative time following the introduction of each intervention. However, a multivariate analysis revealed that team-based strategies, including the use of a standardized nursing team, treatment centralization, and a fellowship program, were significantly associated with a reduction in operative time. Conclusions: The results of this cohort study suggest that among patients with head and neck cancer, use of team-based strategies was associated with significant decreases in operative time without an increase in complications.

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)0.001
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 score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.299
Teacher spread0.268 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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