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Record W3174358275 · doi:10.1097/sla.0000000000004985

Overall Volume Trends in Esophageal Cancer Surgery Results From the Dutch Upper Gastrointestinal Cancer Audit

2021· article· en· W3174358275 on OpenAlexaff
Daan M. Voeten, Suzanne S. Gisbertz, Jelle P. Ruurda, Janneke Wilschut, Lorenzo Ferri, Richard van Hillegersberg, Mark I. van Berge Henegouwen

Bibliographic record

VenueAnnals of Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsMedicineEsophagectomyQuartileOdds ratioConfidence intervalIntensive care unitSurgeryEsophageal cancerCancerGeneral surgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: In the pursuit of quality improvement, this study aimed to investigate volume-outcome trends in oncologic esophagectomy in the Netherlands. SUMMARY OF BACKGROUND DATA: Concentration of Dutch esophageal cancer care was dictated by introducing an institutional minimum of 20 resections/yr. METHODS: This nationwide cohort study included all esophagectomy patients registered in the Dutch Upper Gastrointestinal Cancer Audit in 2016-2019 from hospitals currently still performing esophagectomies. Annual esophagectomy hospital volume was assigned to each patient and categorized into quartiles. Multivariable logistic regression investigated short-term surgical outcomes. Restricted cubic splines investigated if volume-outcome relationships eventually plateaued. RESULTS: In 16 hospitals, 3135 esophagectomies were performed. First volume quartile hospitals performed 24-39 resections/yr; second, third, and fourth quartile hospitals performed 40-53, 54-69, and 70-101, respectively. Compared to quartile 1, in quartiles 2 to 4, overall/severe/technical complication, anastomotic leakage, and prolonged hospital/intensive care unit stay rates were significantly lower and textbook outcome and lymph node yield were higher. When raising the cut-off from the first to second quartile, higher-volume centers had less technical complications [Adjusted odds ratio (aOR): 0.82, 95% confidence interval (CI): 0.70-0.96], less anastomotic leakage (aOR: 0.80, 95% CI: 0.66-0.97), more textbook outcome (aOR: 1.25, 95% CI: 1.07-1.46), shorter intensive care unit stay (aOR: 0.80, 95% CI: 0.69-0.93), and higher lymph node yield (aOR: 3.56, 95% CI: 2.68-4.77). For most outcomes the volume-outcome trend plateaued at 50-60 annual resections, but lymph node yield and anastomotic leakage continued to improve. CONCLUSION: Although this study does not reflect on individual hospital quality, there appears to be a volume trend towards better outcomes in high-volume centers. Projects have been initiated to improve national quality of care by reducing hospital variation (irrespective of volume) in outcomes in The Netherlands.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.137
Threshold uncertainty score0.272

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.176
GPT teacher head0.384
Teacher spread0.207 · 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 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

Citations60
Published2021
Admission routes1
Has abstractyes

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