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Record W2597588095 · doi:10.1002/hed.24758

Systematic review and meta‐analysis of venous thromboembolism in otolaryngology–head and neck surgery

2017· review· en· W2597588095 on OpenAlexaff
Sami P. Moubayed, Antoine Eskander, Moustafa Mourad, Sam P. Most

Bibliographic record

VenueHead & Neck · 2017
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineOtorhinolaryngologyChemoprophylaxisIncidence (geometry)Pulmonary embolismVenous thrombosisOdds ratioSurgeryHead and neck cancerMeta-analysisInternal medicineThrombosisRadiation therapy

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to present our systematic review and meta-analysis of the data on venous thromboembolism (VTE; deep venous thrombosis [DVT] and/or pulmonary embolism [PE]) in otolaryngology-head and neck surgery (OHNS). METHODS: PubMed and Scopus databases were searched for studies reporting VTE in OHNS. Incidence of VTE and bleeding is reported and meta-analyzed overall and for chemoprophylaxis and squamous cell carcinoma (SCC)/free flap subgroups. RESULTS: A total of 23 studies were included with a total of 618,264 patients. Incidence of VTE was 0.4%. The incidence of bleeding complications was 0.9%. The addition of chemoprophylaxis did not result in a decreased VTE incidence (odds ratio [OR], 0.86), but produced an increased risk of bleeding (OR, 3.78). The overall OR for VTE in SCC/free flap cases was 6.28. CONCLUSION: Chemoprophylaxis may not be necessary in the OHNS non-SCC or free flap patient population and must be balanced against an increased risk of bleeding. © 2017 Wiley Periodicals, Inc. Head Neck 39: 1249-1258, 2017.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.035
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0140.016
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.119
GPT teacher head0.388
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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

Citations52
Published2017
Admission routes1
Has abstractyes

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