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Record W4414066502 · doi:10.1002/hed.70040

Flap Complications Following Maxillectomy, Reconstructive Surgery, and Postoperative Proton Radiotherapy: A Cohort Study and Considerations for Risk Mitigation

2025· article· en· W4414066502 on OpenAlexaff
Amir H. Safavi, Fan Yang, Yingzhi Wu, Ian Ganly, Evan Matros, Geoffrey E. Hespe, Tony Hung, Daphna Y. Gelblum, Marc A. Cohen, Richard J. Wong, Brian Shen, Kevin Sine, Dong Soo Han, Dennis Mah, Nancy Y. Lee

Bibliographic record

VenueHead & Neck · 2025
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComplicationCohort studyRisk assessmentSelection (genetic algorithm)CohortRetrospective cohort studyRisk management

Abstract

fetched live from OpenAlex

BACKGROUND: Flap complications following maxillectomy, reconstruction, and adjuvant proton beam therapy (PBT) for primary maxillary and sinonasal malignancies are not well described. METHODS: This retrospective cohort study included consecutive patients treated between 2016 and 2023 from a single-institutional database. RESULTS: Thirteen patients were identified with a median follow-up of 26 months. No immediate post-operative complications occurred, with a median time between surgery and PBT of 61 days. Nine patients (69.2%) had high-risk postoperative pathology and required 66+ Gy(RBE), including five (38.5%) with gross disease receiving 70 Gy(RBE). Flap necrosis and fistulization occurred in three patients (23.1%) following PBT (median time from PBT to complication, 112 days). Higher flap mean and minimum dose, flap volume receiving at least 66 Gy[RBE], and minimum dose to 50% flap volume were associated with complications. CONCLUSIONS: Careful patient selection and planning strategies should be considered to mitigate flap complication risk following adjuvant PBT for high-risk post-operative pathology.

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)0.001
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 score0.008
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.026
GPT teacher head0.338
Teacher spread0.313 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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