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Record W1527389771 · doi:10.1002/cncr.23497

Measuring the significance of workload on performance of cytotechnologists in gynecologic cytology

2008· article· en· W1527389771 on OpenAlexaff
Majorie Deschênes, Andrew A. Renshaw, Manon Auger

Bibliographic record

VenueCancer · 2008
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineWorkloadCytologyGynecologyObstetricsColposcopyPathologyInternal medicineCervical cancerCancer

Abstract

fetched live from OpenAlex

BACKGROUND: Workload is extensively regulated and often used as a measure of quality in gynecologic cytology. Whether workload correlates with the sensitivity of screening in gynecologic cytology is not known. METHODS: The sensitivity of gynecologic cytology screening was measured over an 8-month period using the result of full screening coupled with the results of rapid prescreening. Sensitivity results were then correlated with daily workload volumes and the experience level of individual cytotechnologists. RESULTS: Rapid prescreening had an average sensitivity of 41.9% for atypical squamous cells of undetermined significance (ASCUS) and above. Full screening had a corrected sensitivity of 82.2% for ASCUS and above. Rapid prescreening increased the sensitivity of the laboratory to 89.9%. The sensitivity of full screening was significantly different between cytotechnologists (79.2% vs 99%, P < .001), but was not correlated with years of experience, sensitivity of rapid prescreening, or workload (all P > .05). When sensitivity and workload were examined on a monthly basis, there was no significant difference between sensitivity either as a group or individually at the highest and lowest workloads (P > .40 for all). CONCLUSIONS: Screeners sensitivity in gynecologic cytology appears to be unrelated to the experience level of individual cytotechnologists or to their workload at the levels examined.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.662

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)0.0010.000

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 head0.107
GPT teacher head0.330
Teacher spread0.223 · 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

Citations29
Published2008
Admission routes1
Has abstractyes

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