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Record W1489724758 · doi:10.1111/ans.12786

Outcomes in the surgical treatment of low rectal cancer: does neoadjuvant treatment equalize results?

2014· article· en· W1489724758 on OpenAlexaff
William G. Pollett, Peter Gibbs, Stephen McLaughlin, Jimmy Eteuati, Michael Harold, Kaye Marion, Shweta N. Patel, Ian T. Jones

Bibliographic record

VenueANZ Journal of Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineRectumColorectal cancerSurgeryNeoadjuvant therapyRadiation therapyCancerInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The choice of operation for potentially curable cancer of the low rectum (≤6 cm from the anal verge) is usually between ultra low anterior resection (ULAR) or abdominal perineal excision (APE). Numerous studies have suggested improved results with ULAR. METHODS: This study was a retrospective review of prospectively collected data for a series of patients undergoing surgical treatment for low rectal cancer at three Melbourne hospitals. The patient details and outcomes were compared between those undergoing APE and ULAR. RESULTS: One hundred and ninety-eight of 213 patients with potentially curable low rectal cancer were treated by either ULAR (n = 82) or APE (n = 116). Overall survival and local recurrence rates were similar, although there was a trend towards improved results for ULAR. Preoperative radiation was received by 89 (76.7%) of APE patients and 44 (53.7%) of ULAR patients (P < 0.0005). CONCLUSION: In this study there was no statistical difference in the oncological results between APE and ULAR. However, there was a trend to improved result for ULAR in spite of a strikingly higher rate of neoadjuvant radiation in the APE group. It is possible that enhanced use of preoperative radiation has a beneficial role in the management of low rectal cancer treated by conventional APE.

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)0.001
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 score0.175
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.060
GPT teacher head0.349
Teacher spread0.288 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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