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Record W1965059048 · doi:10.1309/08j6mdlcjpphjnd1

Interobserver Variability in Assessing Adequacy of the Squamous Component in Conventional Cervicovaginal Smears

2003· article· en· W1965059048 on OpenAlexfundno aff
Matthew V. Sheffield, Aylin Simsir, Lynya Talley, Andrea Roberson, Paul Elgert, David C. Chhieng

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2003
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsnot available
FundersYork UniversityEmory University
KeywordsKappaPapanicolaou stainMedicineReproducibilityPap smearsStatisticsCohen's kappaBethesda systemReference valuesGynecologyNuclear medicineMathematicsPathologyInternal medicineCytologyCancerCervical cancer

Abstract

fetched live from OpenAlex

We compared the interobserver reproducibility of estimating the adequacy of the squamous component of conventional Papanicolaou (Pap) smears using traditional and newly proposed criteria. Forty conventional Pap smears with varying degrees of squamous cellularity were reviewed by 13 observers who evaluated adequacy (satisfactory vs unsatisfactory) based on the traditional criterion of estimating 10% slide coverage. After being introduced to the new criterion and the reference images, the observers reevaluated adequacy on the same set of smears, using the new criterion and the reference images. With the original criterion of 10% slide coverage, 15 smears had a unanimous designation; the overall kappa value was 0.49 (P < .001). With the newly proposed adequacy criterion and reference images, 17 smears had a unanimous designation; the overall kappa value was 0.60 (P < .001). The difference in the kappa correlation coefficients was statistically significant (P = .007). While traditional and newly proposed criteria resulted in fair interobserver agreement, it seemed that the newly proposed criterion, along with the use of reference images, for evaluating adequacy of the squamous component of conventional Pap smears results in better interobserver reproducibility.

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)0.007
metaresearch head score (Gemma)0.005
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 score0.037
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.087
GPT teacher head0.454
Teacher spread0.367 · 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

Citations17
Published2003
Admission routes1
Has abstractyes

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