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Record W2032451896 · doi:10.1007/s00268-013-2182-7

A Square Peg in a Round Hole? Challenges with DALY‐based “Burden of Disease” Calculations in Surgery and a Call for Alternative Metrics

2013· article· en· W2032451896 on OpenAlexaff
Richard A. Gosselin, Doruk Ozgediz, Dan Poenaru

Bibliographic record

VenueWorld Journal of Surgery · 2013
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineMetric (unit)Disease burdenBurden of diseaseHealth careVascular surgeryDiseaseCardiac surgeryOperations managementSurgeryPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: In recent years, surgical providers and advocates have engaged in a growing effort to establish metrics to estimate capacity for surgical services as well the burden of surgical diseases in resource-limited settings. The burden of disease (BoD) studies have established the disability-adjusted life year (DALY) as the primary metric to measure both disability and premature mortality. Nonetheless, DALY-based approaches present methodological challenges, some of which are unique to surgical conditions, not fully addressed through the multiple iterations of the BoD studies, including the most recent study. METHODS AND RESULTS: This paper examines these challenges in detail, including issues around age-weighting and discounting, and estimates of disability-weights for specific conditions. Surgical burden measurements of specific conditions, or through the assessment of hospital wards as platforms for service delivery, still have unresolved methodological hurdles. The 2010 BoD study addresses some of these issues, but many questions still remain. Other methods estimating surgical prevalence, backlogs in treatment, and disability incurred by delays in care may provide more practical approaches to disease burden that can be useful tools for clinicians and health advocates. CONCLUSIONS: These approaches warrant further exploration in LMICs and these debates require active engagement by surgical providers and advocates globally.

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)0.002
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 score0.016
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.069
GPT teacher head0.313
Teacher spread0.243 · 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

Citations33
Published2013
Admission routes1
Has abstractyes

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