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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".