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Record W2548040535 · doi:10.1002/jso.24492

Utilization of pre‐operative imaging for colon cancer: A population‐based study

2016· article· en· W2548040535 on OpenAlexaffabout
Matthew D. F. McInnes, Sulaiman Nanji, William J. Mackillop, Jennifer A. Flemming, Xuejiao Wei, D. Blair Macdonald, Nicola Scheida, Christopher M. Booth

Bibliographic record

VenueJournal of Surgical Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsQueen's UniversityOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineAbdomenGuidelinePoisson regressionPelvisPopulationRetrospective cohort studyClinical PracticeColorectal cancerCancerGeneral surgeryRadiologySurgeryInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the use of pre-operative imaging for colon cancer and to identify factors associated with utilization in routine clinical practice. METHODS: This population-based, retrospective cohort study used a random sample of 25% of colon cancer patients treated with surgery in the province of Ontario (2002-2008). Pre-operative imaging (<16 weeks from surgery) of the chest, abdomen-pelvis was identified. Modified poisson regression was used to analyze factors associated with practice patterns. RESULTS: Of the 7,249 included patients, 48% had pre-operative imaging (CT abdomen and imaging of the chest) in keeping with guideline recommendations. The rate of guideline concordant pre-operative imaging increased over time: 64% in the most recent study period (2006-2008) versus 31% (2002-2004); P < 0.001. Variables associated with use of chest imaging: Age, co-morbidity, surgeon volume, and geographic region; no association with gender, hospital volume, or socio-economic status. Variables associated with use of abdomen imaging: Hospital volume and geographic region; no association with age, gender, comorbidity, socio-economic status, or surgeon volume. CONCLUSION: In clinical practice, the majority of patients were not receiving pre-operative imaging that was in line with clinical practice guidelines; however, use increased over time indicating a possible association with dissemination of clinical practice guidelines. J. Surg. Oncol. 2017;115:202-207. © 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 score0.187
Threshold uncertainty score0.380

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 head0.062
GPT teacher head0.432
Teacher spread0.370 · 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

Citations7
Published2016
Admission routes2
Has abstractyes

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