MétaCan
Menu
Back to cohort
Record W2885824678 · doi:10.1016/j.pmedr.2018.08.003

The fecal immunochemical test (fit): Selected aspects regarding its effectiveness for colorectal cancer screening in Quebec City

2018· article· en· W2885824678 on OpenAlexaffabout
Mireille Caron, Gabriel Lamarre, Philippe Grégoire, David Simonyan, Nathalie Laflamme

Bibliographic record

VenuePreventive Medicine Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsCentre hospitalier universitaire de QuébecHôpital Saint-François d'AssiseUniversité Laval
Fundersnot available
KeywordsMedicineColorectal cancerAnemiaColonoscopyMedical prescriptionTest (biology)Iron-deficiency anemiaCancerInternal medicineColorectal cancer screeningGastroenterologyFamily medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: FIT's value has been ascertained across Canada and worldwide, but still needs to be assessed within the province of Quebec. There also remains a gap between formal indications for FIT, and its actual use in clinical practice. This research aims to evaluate some aspects of FIT's effectiveness in our setting, and its application by prescribers. METHODS: We retrospectively identified and reviewed all the colonoscopies conducted for a positive FIT in 2014 at 2 hospitals located in Quebec City. RESULTS: Five hundred and fifty-nine (559) colonoscopies were reviewed. We obtained PPVs of 6.8% and 46.9% for the detection of CRC and AA, respectively. The PPV for the detection of SCL was higher in men compared to women (OR 1.56, 95%CI 1.11-2.20) and among justified FITs compared to unwarranted ones (OR 1.88, 95%CI 1.34-2.63). The PPV for CRC detection was 25.0% in the presence of unexplained iron deficiency anemia and 6.5% when anemia was absent (p = 0.0058). In 49.9% of cases, the prescription of a FIT was inappropriate. CONCLUSION: The FIT holds a better PPV for detecting SCL among men and when it is indicated. Anemia is associated with a higher CRC detection rate. Half of the FITs were not initially indicated.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.161
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.330
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 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

Citations5
Published2018
Admission routes2
Has abstractyes

Explore more

Same venuePreventive Medicine ReportsSame topicColorectal Cancer Screening and DetectionFrench-language works237,207