Measuring the significance of workload on performance of cytotechnologists in gynecologic cytology
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".