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Should lymph node retrieval be a surgical quality indicator in colon cancer?

2012· article· en· W2589716546 on OpenAlexaff
Reilly Musselman, Mingyang Xie, Kelsey McLaughlin, Husein Moloo, Robin P. Boushey, Rebecca A. Auer

Bibliographic record

VenueJournal of Clinical Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineColorectal cancerLymph nodeLogistic regressionUnivariate analysisMultivariate analysisUnivariateLymphStage (stratigraphy)SurgeryDissection (medical)CancerOncologyMultivariate statisticsInternal medicinePathologyStatistics

Abstract

fetched live from OpenAlex

648 Background: Adequate lymph node harvest is easily obtained and is necessary for proper staging of colorectal cancer, making it an attractive measure of surgical quality for policy makers. However, achieving an adequate lymph node harvest requires a multidisciplinary effort. The purpose of this study was to determine if it is appropriate to use this measure as a surgical quality indicator for individual surgeons. Methods: The study was undertaken at a high volume center with standardized colon cancer specimen processes. The charts of 1,138 consecutive segmental colon cancer surgeries performed between 2002 and 2008 were retrospectively analyzed. The primary outcome was inadequate lymph node retrieval for colon cancer surgery defined by fewer than 12 lymph nodes on pathology. Predictor variables were based on patient, surgeon, pathology and tumor related factors. Univariate analysis was performed on all potential predictor variables, followed by multivariate logistic regression. Results: 841 cases (69.0%) achieved adequate lymph node harvest, while 377 (31.0%) were inadequate. Factors on univariate analysis associated with inadequate lymph node harvest were specimen length (p<0.0001), tumor location (p<0.0001), and T-stage (0.0015), all of which remained significant multivariate logistic regression. The average specimen length differed by 3.6 cm between non-adequate and adequate specimens. When broken down by procedure, resection length did not vary significantly between high and low volume surgeons or between colorectal and non-colorectal surgeons. Furthermore when surgeons were ranked according to their success rate of >12 LN retrieval, there was no difference between surgeons in mean specimen length. Conclusions: In a high-volume, tertiary care centre that uses standardized practices for specimen processing, 31% of cases yielded fewer than 12 lymph nodes. Factors relating to the patient and tumor were the primary predictors of a successful outcome and there was no association between surgeon-related factor and adequate LN retrieval. Caution should be used when considering LN harvest as a surgical quality indicator for individual surgeons.

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)0.020
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 score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.349
GPT teacher head0.571
Teacher spread0.222 · 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".

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Citations2
Published2012
Admission routes1
Has abstractyes

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