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S322 Sessile Serrated Lesion Detection Rate in Individuals with Positive Fecal Immunohistochemical Test Undergoing Colonoscopy: A Systematic Review and Meta-Analysis

2023· review· en· W4387749886 on OpenAlexaboutno aff
Fouad Jaber, Mouhand Mohamed, Natalie Wilson, Abubaker Abdalla, Abdelrahman Mohamed Mahmoud, Saqr Alsakarneh, Azizullah Beran, Khalid K. Ahmed, Aasma Shaukat, Mohammad Bilal

Bibliographic record

VenueThe American Journal of Gastroenterology · 2023
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisColonoscopyColorectal cancerInternal medicineStudy heterogeneityConfidence intervalColorectal cancer screeningFecesGastroenterologyCancer

Abstract

fetched live from OpenAlex

Introduction: Fecal immunochemical test (FIT) is a stool-based test recommended by the United States Preventative Services Taskforce (USPSTF) for screening for colorectal cancer (CRC) in average risk individuals. While studies have reported adenoma detection rates in FIT-positive individuals, there is a lack of data on the performance of FIT in detecting sessile serrated lesions (SSLs). We conducted a systematic review and meta-analysis to evaluate the SSL detection rate in individuals with a positive FIT undergoing colonoscopy. Methods: We conducted a comprehensive search of PubMed, Scopus, and Embase databases up to May 2023 to identify studies reporting SSLs in FIT-positive individuals undergoing colonoscopy for average-risk CRC screening. The primary outcome was the overall SSL detection rate, and secondary outcomes included the prevalence of proximal and distal SSLs. A proportion meta-analysis, utilizing the random effects model, was used to generate the pooled rates with their corresponding 95% confidence intervals (CI). I2 adjudicated the heterogeneity. CMA software was used for statistical analysis. Results: Six studies met the inclusion criteria, involving 252,455 FIT-positive individuals undergoing colonoscopy. Average age of patients ranged between 59-62 years, with 45.5% being female. Four of six studies were from North America (US and Canada). Two studies used a FIT positivity threshold of 20 mcg /g feces, while the remaining studies used a threshold of 10 mcg /g feces. A total of 14,026 individuals were found to have at least 1 SSL from a total of 252,455 patients across six studies, for a pooled SSL detection rate of 5.56%. Using random-effects models, the overall SSL detection rate was estimated to be 4.91% (CI: 3.14% - 7.60%; I2: 99%) (Figure 1A). For proximal SSLs, the detection rate was 3.49% (CI: 0.77% - 14.37%; I2: 99%), based on three studies (Figure 1B). For distal SSLs detection, the pooled rate from three studies was 2.05% (CI: 1.17% - 3.57%; I2: 99%) (Figure 1C). Conclusion: Our meta-analysis highlights sessile serrated lesion (SSL) detection rates in FIT-positive individuals undergoing colonoscopy. The findings reveal a significant and variable prevalence of SSLs in this population, with rates differing based on polyp location. Our analysis provides valuable information regarding SSL detection rates in FIT-positive individuals which can be used as a quality benchmark in FIT based population screening programs.Figure 1.: A: Forest plot for metanalysis about prevalence of SSLs among patients with +FIT test undergoing colonoscopies, B: Forest plot for metanalysis about prevalence of proximal SSLs among patients with +FIT test undergoing colonoscopies, C: Forest plot for metanalysis about prevalence of distal SSLs among patients with +FIT test undergoing colonoscopies.

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)0.010
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.030
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0160.033
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.046
GPT teacher head0.347
Teacher spread0.301 · 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 designMeta-analysis
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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