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Record W2998519812 · doi:10.1155/2019/5430786

Vascularized Free Tissue Transfer in a Patient with Hemophilia B: Case Report and Literature Review

2019· article· en· W2998519812 on OpenAlexaff
Mohammed Qaisi, Justin Kierce, James J. Murphy

Bibliographic record

VenueCase Reports in Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineHematologistSurgeryPerioperativeCoagulationHemostasisFree flapCoagulation cascadePlateletInternal medicine

Abstract

fetched live from OpenAlex

Hemophilia is a blood disorder characterized by impairment of the coagulation cascade leading to an increased bleeding risk (Kauffman, 2014). As such, surgical management of these patients can become difficult and well-defined surgical guidelines are not yet in place (Assoumane et al., 2017). Close monitoring of perioperative factor levels may be even more crucial for those undergoing microvascular free tissue transfer. This is because either a hypercoagulable or hypocoagulable bleeding state has the potential to further increase the risk of vascular compromise to the flap. We report a successful case of mandibular reconstruction using a free fibular flap in a patient with severe hemophilia B and the protocols we used, as well as a review of the literature of similar cases. In the literature, we identified 6 cases of microvascular free tissue transfer in patients with hemophilia; two of these cases had complications which were both related to excess bleeding. It is crucial that these cases be managed in a multidisciplinary fashion in close consultation with a hematologist. The role of venothromboembolism (VTE) prophylaxis in the hemophilic patient undergoing free tissue transfer is discussed.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.280
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations2
Published2019
Admission routes1
Has abstractyes

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