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Record W2095442012 · doi:10.1155/2013/379564

The Economic Impact of Weight Regain

2013· review· en· W2095442012 on OpenAlexaffabout
Caroline E. Sheppard, E Lester, Anderson Chuck, Daniel W. Birch, Shahzeer Karmali, Christopher J. de Gara

Bibliographic record

VenueGastroenterology Research and Practice · 2013
Typereview
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsInstitute of Health EconomicsUniversity of Alberta
Fundersnot available
KeywordsMedicineRecidivismObesitySurgeryWeight lossCost–benefit analysisTotal costEconomic costCost analysisPsychiatryOperations researchInternal medicineEconomicsEngineering

Abstract

fetched live from OpenAlex

Background. Obesity is well known for being associated with significant economic repercussions. Bariatric surgery is the only evidence-based solution to this problem as well as a cost-effective method of addressing the concern. Numerous authors have calculated the cost effectiveness and cost savings of bariatric surgery; however, to date the economic impact of weight regain as a component of overall cost has not been addressed. Methods. The literature search was conducted to elucidate the direct costs of obesity and primary bariatric surgery, the rate of weight recidivism and surgical revision, and any costs therein. Results. The quoted cost of obesity in Canada was $2.0 billion-$6.7 billion in 2013 CAD. The median percentage of bariatric procedures that fail due to weight gain or insufficient weight loss is 20% (average: 21.1% ± 10.1%, range: 5.2-39, n = 10). Revision of primary surgeries on average ranges from 2.5% to 18.4%, and depending on the procedure accounts for an additional cost between $14,000 and $50,000 USD per patient. Discussion. There was a significant deficit of the literature pertaining to the cost of revision surgery as compared with primary bariatric surgery. As such, the cycle of weight recidivism and bariatric revisions has not as of yet been introduced into any previous cost analysis of bariatric 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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.902
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.142
GPT teacher head0.479
Teacher spread0.337 · 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 designNot applicable
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

Citations46
Published2013
Admission routes2
Has abstractyes

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