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Record W2475185387 · doi:10.1002/jso.24334

A population‐based comparison of 30‐day readmission after surgery for colon and rectal cancer: How are they different?

2016· article· en· W2475185387 on OpenAlexaffabout
Aristithes G. Doumouras, Miriam Tsao, Fady Saleh, Dennis Hong

Bibliographic record

VenueJournal of Surgical Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsMedicineColorectal cancerStoma (medicine)Internal medicineColectomyCohortRetrospective cohort studyCancerGastroenterologySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: An implicit assumption in the analysis of colorectal readmission is that colon and rectal cancer patients are similar enough to analyze together. However, no studies have examined this assumption and whether substantial differences exist between colon and rectal cancer patients. METHODS: This was a retrospective analysis of the differences in predictors, diagnoses, and costs of readmission between colon and rectal cancer cohorts for 30-day readmission. This study included all patients aged >18 who received an elective colectomy or low anterior resection for colorectal cancer from April 2008 until March 2012 in the province of Ontario. RESULTS: Overall, 13,571 patients were identified and the readmission rates significantly differed between rectal and colon cancer patients (7.1% colon and 10.7% rectal P = 0.001). Diabetes, age, and discharge to long term care were significantly different among colon and rectal patients in the prediction of readmission. Readmission for renal and stoma causes was more prominent in the rectal cohort. The adjusted cost difference for readmission did not significantly differ between rectal and colon cancer $178 ($1,924-1,568 P = 0.84) CONCLUSION: Several important differences in predictors and diagnoses exist between the two cohorts. Conversely, the costs associated with readmission were homogenous between rectal and colon cancer patients. J. Surg. Oncol. 2016;114:354-360. © 2016 Wiley Periodicals, Inc.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.376
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.045
GPT teacher head0.353
Teacher spread0.307 · 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

Citations20
Published2016
Admission routes2
Has abstractyes

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