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Recurrence and Carcinogenetic Rates of Colorectal Polyps

2024· article· en· W4392665452 on OpenAlexaff
Marilena Stoian, Gabriel Becheanu, Irina Bălescu, Mihai Eftimie, Sorin Petrea, Camelia Cristina Diaconu, Bogdan Gașpar, Lucian Pop, Valentin Varlas, Adrian Hașegan, Cristina Martac, Ciprian Bolca, Cezar Stroescu, Anca Zgură, Nicolae Bacalbașa

Bibliographic record

VenueChirurgia · 2024
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsHôpital Charles-Le MoyneUniversité de Sherbrooke
Fundersnot available
KeywordsMedicinePolypectomyColonoscopyMalignant transformationAdenomatous polypsGastroenterologyNeoplastic transformationInternal medicineAdenomaResectionSurgeryColorectal cancerCancerPathology

Abstract

fetched live from OpenAlex

AIM: to determin the recurrence rate of benign recto-colonic polyps in a 5-year interval, and compare the development rate of intrapolypoid carcinomatous lesions in polypectomized versus nonpolypectomized subjects. MATERIAL AND METHOD: The recurrence rate of polyps increased annually from 5 to 12.5%; the highest rate was noted in the last two years. The five-year cumulative risk of neoplastic lesions was 73% in patients without polypectomy and 20% among those with endoscopic resection (p 0.05). Comparing the recurrence rate of benign lesions (60%) in patients without neoplastic findings with the recurrence rate of adenomas in patients with benign lesions (40%), a higher risk of recurrence was found in the first category, and seemed to be influenced by the personal history of pre-existing adenomatous lesions. CONCLUSION: an increased risk of colorectal polyps recurrence was reported during five year follow up; moreover, during the first three years an increased risk of malignant transformation was observed among cases in which endoscopic resection was not feasible when compared to those in which complete excision was feasible.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.019
GPT teacher head0.296
Teacher spread0.276 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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