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Record W2964377244 · doi:10.1002/hed.25885

Postoperative infection predicts poor survival in locoregionally advanced oral cancer

2019· article· en· W2964377244 on OpenAlexafffund
Marco A. Mascarella, Jayson Lee Azzi, Sabrina Daniela da Silva, Alex Mlynarek, Véronique‐Isabelle Forest, Michael Hier, Keith Richardson, Nader Sadeghi

Bibliographic record

VenueHead & Neck · 2019
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsJewish General HospitalUniversity of OttawaMcGill UniversityMcGill University Health Centre
FundersFonds de Recherche du Québec - Santé
KeywordsMedicineConfidence intervalPropensity score matchingHazard ratioProportional hazards modelConfoundingSurgeryStage (stratigraphy)Retrospective cohort studyCancerInternal medicineSurvival analysis

Abstract

fetched live from OpenAlex

BACKGROUND: We ascertain the association of postoperative infection on survival in patients with locoregionally advanced oral cavity squamous cell carcinoma (OCSCC). METHODS: A retrospective study of patients with stage III/IVA OCSCC undergoing curative-intent surgery was performed. Postoperative infection was considered within 30 days after surgery. Kaplan-Meier survival curves were used to compare overall survival (OS) in patients with postoperative infection. Cox regression and propensity-score matching were used to adjust for confounders. RESULTS: Fifty-four of 114 patients had a postoperative infection. The 5-year OS in patients with a postoperative infection (24.1%) was lower than those without (65.2%; P < .0001). Postoperative infection was a negative predictor of OS after adjusting for patient, antibiotic, pathologic, and operative factors; the adjusted hazard ratio for OS was 2.54 (95% confidence interval, 1.27-5.09). CONCLUSION: Postoperative infection is a strong negative predictor of OS in patients with OCSCC undergoing ablative surgery.

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)0.000
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 score0.049
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.028
GPT teacher head0.331
Teacher spread0.303 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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