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Record W2977075722 · doi:10.1089/bari.2019.0015

Abdominal Panniculectomy After Bariatric Surgery: An Unmet Need in the Bariatric Population

2019· article· en· W2977075722 on OpenAlexaffabout
Francesca Seal, Isaiah MacDonald, Christopher de Gara, David Lesniak

Bibliographic record

VenueBariatric Surgical Practice and Patient Care · 2019
Typearticle
Languageen
FieldMedicine
TopicBody Contouring and Surgery
Canadian institutionsUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineSurgeryAbdominoplastyWeight lossGeneral surgerySpecialtyGastric bypassPlastic surgeryObesityFamily medicine

Abstract

fetched live from OpenAlex

Introduction: Panniculectomies are performed relatively infrequently despite demand for this procedure among bariatric surgery patients. Materials and Methods: In this multicomponent study, a survey of postbariatric surgery patients more than 6 months postop was distributed at the Edmonton Adult Bariatric Specialty Clinic from July 2017 to April 2018 and a survey of 245 plastic, bariatric, and general surgeons in the province of Alberta was administered online. Results: Of 87 postbariatric surgery patients surveyed, 90.6% were satisfied with the result of their bariatric surgery, yet 69.1% reported at least one issue relating to excess skin and 90.7% were interested in undergoing panniculectomy for excess abdominal skin. Among 22 general and 11 plastic surgeons surveyed, 41% of whom reported performing panniculectomy, 74% agreed that postbariatric panniculectomy is a medical necessity, but the majority of surgeons not already performing panniculectomy would not include it in their practice due to lack of interest, lack of operating room time, and inadequate financial compensation. Conclusion: There is significant demand for panniculectomy, but few surgeons are interested in performing panniculectomy, leaving a considerable care gap.

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)0.001
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 score0.057
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.259
Teacher spread0.248 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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