Livmorhalsprøvetaking i primærhelsetjenesten
Bibliographic record
Abstract
BACKGROUND: In Norway, approximately 360 000 cervical screening samples were taken in 2020, of which 11 000 were registered as inadequate. We therefore wished to investigate doctors' knowledge of cervical sample-taking in the primary health service. MATERIAL AND METHOD: An anonymous survey on cervical sample-taking was sent by email to around 4 700 members of the Norwegian College of General Practice in September 2021. RESULTS: Of the 1 039 doctors who responded to the survey, 820 (79 %) reported that they always indicate the reason for taking the sample in the requisition form, and 898 (86 %) reported that they avoid taking a sample during menstruation. Only one in three doctors (343) correctly indicated the location of the squamocolumnar junction in postmenopausal women. In response to a question aimed at users of the ThinPrep method, which is particularly sensitive to sampling errors, 426 out of 697 (61 %) answered that they either avoid using a lubricant or use a water-based lubricant, while only 35 % of the doctors responded that they stop taking the sample if bleeding occurs. INTERPRETATION: The results show that although many doctors have satisfactory knowledge, a continuous focus on cervical sample-taking is essential. Correct sampling and knowledge of anatomical factors in postmenopausal women may be significant for reducing the number of inadequate samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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; both teacher heads agree on what is shown here.
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".