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Record W2289101218 · doi:10.1177/0969141315610790

Audit feedback on reading performance of screening mammograms: An international comparison

2016· article· en· W2289101218 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Medical Screening · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsInstitut National de Santé Publique du QuébecHôpital du Saint-Sacrement
FundersNational Cancer Institute
KeywordsMedicineAuditMammographyScreening mammographyRecall rateQuality assuranceBreast screeningRecallMedical physicsBreast cancerCancerAccountingPathologyExternal quality assessmentPsychologyComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: Providing feedback to mammography radiologists and facilities may improve interpretive performance. We conducted a web-based survey to investigate how and why such feedback is undertaken and used in mammographic screening programmes. METHODS: The survey was sent to representatives in 30 International Cancer Screening Network member countries where mammographic screening is offered. RESULTS: Seventeen programmes in 14 countries responded to the survey. Audit feedback was aimed at readers in 14 programmes, and facilities in 12 programmes. Monitoring quality assurance was the most common purpose of audit feedback. Screening volume, recall rate, and rate of screen-detected cancers were typically reported performance measures. Audit reports were commonly provided annually, but more frequently when target guidelines were not reached. CONCLUSION: The purpose, target audience, performance measures included, form and frequency of the audit feedback varied amongst mammographic screening programmes. These variations may provide a basis for those developing and improving such programmes.

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.

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)0.002
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 score0.620
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.095
GPT teacher head0.386
Teacher spread0.291 · 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