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Record W2092693807 · doi:10.1177/1553350611415868

Midterm Outcomes of Laparoscopic Surgery for Rectal Cancer

2012· article· en· W2092693807 on OpenAlexaff
Jessica Westerholm, Sandra García-Osogobio, Forough Farrokhyar, Margherita Cadeddu, Mehran Anvari

Bibliographic record

VenueSurgical Innovation · 2012
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsMedicineLaparoscopic surgerySurgeryGeneral surgeryColorectal cancerLaparoscopyCancer surgeryCancerInternal medicine

Abstract

fetched live from OpenAlex

In this study, the authors examine midterm survival and recurrence after laparoscopic and open surgery for rectal cancer. This is a retrospective review of a prospective database for rectal cancer surgeries performed at the authors' institution, with follow-up data obtained through chart review. In all, 74 patients in this study had open surgery, and 93 had laparoscopic surgery. The 5-year overall survival was 73.6% ± 12.0% in the open group and 80.0% ± 12.8% in the laparoscopic group (P = .159). Disease-free survival at 5 years was better in the laparoscopic group (71.0% ± 13.4%) than in the open group (50.3% ± 12.7%), with a P value of .01. Laparoscopic surgery remained an independent predictor of disease-free survival in the multivariate analysis. Results of prospective randomized trials are awaited, and the authors expect that the laparoscopic approach will be shown to be a safe and effective option for the management of rectal cancer.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.083
GPT teacher head0.387
Teacher spread0.305 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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