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Comparing Advancement and Coverage of Anterograde Homodigital Neurovascular Island Flap Designs for Volar Fingertip Injuries Using a Cadaveric Model

2020· article· en· W3019739747 on OpenAlexaff
Michelle Woitowich, Celine Yeung, Chris Doherty, Paul Binhammer, Tyler S. Beveridge

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsUniversity of British ColumbiaUniversity of TorontoWestern University
Fundersnot available
KeywordsNeurovascular bundleCadaveric spasmMedicineSurgery3d printedSensationBiomedical engineering

Abstract

fetched live from OpenAlex

Background Following injury, normal interaction between the hand and its surrounding environment is dependent on the ability of the surgical intervention to maintain fingertip sensation. To do this, plastic surgeons can use the anterograde homodigital neurovascular island flaps (a‐HNIF) ‐ a technique that uses a pedicled skin flap (i.e., a flap that maintains its neurovascular supply) ‐ to reconstruct a fingertip with sensation. The triangular and step‐wise flap shapes have been described as suitable flap shapes; however, it remains unclear how much advancement and coverage can be expected from either as patient‐specific factors such as tissue elasticity, wound and hand size have limited comparison of the techniques in the clinic. Aim & Objectives To control for these patient‐specific factors, this study uses paired, fresh (not embalmed) cadaveric hands as a model to investigate the amount of flap advancement and wound coverage provided by the triangular and step‐wise a‐HNIF. Comparison of the triangular and step‐wise flap designs was first examined by incrementally increasing proximal dissection to determine the amount of advancement afforded by each of the a‐HNIF techniques (Objective 1). Subsequently, we investigated which flap design ‐ when sutured in place ‐ provides the greatest wound coverage (Objective 2). Methodology Using paired digits (2–5), flap designs (triangular or step‐wise) were assigned in an alternating pattern to control for possible confounding patient‐specific factors (Fig. ). All procedures were performed by a single investigator and 3D printed guides were used to standardize the flap designs as well as the injury elicited to each finger (p>0.05). For every 5mm of proximal dissection of the pedicle, the distance of advancement was measured while using a consistent force of tension (μtension = 0.31 ± 0.02N; p>0.05). Following 30mm of proximal dissection (i.e., approximately the origin of the digital arteries in the palm), the flap was sutured in place to cover the elicited injury and the percent wound coverage was calculated. Results Our preliminary results (n=14 digits) indicate that the triangular flap achieved significantly more advancement than the step‐wise flap at ≥25mm of proximal dissection into the hand (p<0.05). At 30mm of proximal dissection, the step‐wise flap provided 75.9 ± 6.9% wound coverage whereas the triangular flap covered 80.1 ± 6.1% of the wound (p>0.05). These findings suggest that both the triangular and step‐wise flap designs offer comparable reconstructive outcomes; however, triangular flaps may be able to achieve these results with less proximal dissection. Implications Our study provides the first specimen‐matched comparison of advancement and coverage provided by the triangular and step‐wise a‐HNIF. The results of this study will help inform flap selection to improve outcomes for patients undergoing volar fingertip reconstruction. Support or Funding Information None Figure 1

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.067
GPT teacher head0.279
Teacher spread0.212 · 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 designBench or experimental
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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