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Record W3196684238 · doi:10.37897/rjmp.2016.4.4

The impact of preoperative preparation on postoperative complications in patients with colorectal cancer

2016· article· en· W3196684238 on OpenAlexaff
Octavia Cristina Rusu, Radu Costea, Cristian Constantin Popa, S Neagu

Bibliographic record

VenueRomanian Journal of Medical Practice · 2016
Typearticle
Languageen
FieldMedicine
TopicStoma care and complications
Canadian institutionsLaurentian University
Fundersnot available
KeywordsMedicineColorectal cancerBowel preparationCancerColorectal surgeryDiseaseClinical trialSurgeryGeneral surgeryIntensive care medicineInternal medicineAbdominal surgeryColonoscopy

Abstract

fetched live from OpenAlex

Colorectal cancer is a frequently encountered disease. In most countries, it represents the second leading cause of cancer death. The treatment with radical intent of this condition is surgical. Objective: Through this study, we want to update some data regarding the impact of nutrition and the preoperative mechanical bowel preparation on postoperative complications, in patients who need surgical treatment for colorectal cancer. Material and Method: Relevant articles in the field, contained in international databases were analysed, with no language exclusion, including clinical trials and meta-analyses performed between 1994 and 2015. Conclusions: Preoperative preparation is particularly important in the postoperative evolution of the patients with colorectal cancer and it is based on several main principles: nutritional support, antimicrobial treatment and mechanical bowel preparation.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.367
Teacher spread0.352 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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