Dilemma: wel of niet testen op lymeziekte in de huisartsenpraktijk?
Bibliographic record
Abstract
There is no such thing as a perfect diagnostic test and the value of a test depends on the situation in which the test is being used. Here, we discuss two options for dealing with the diagnostic process for Lyme borreliosis in general practice. One option is to manage, treat or refer according to clinical signs and symptoms, in accordance with Dutch practice guidelines. The other option is to use laboratory tests to guide further patient management (treatment or referral). The choice depends on currently unknown factors, such as the pre-test probability of Lyme disease in patients presenting to general practitioners. Furthermore, clarity is required about how to proceed after a positive or negative test result. The consequences of a false test result will depend on the patient's status, possible alternative diagnoses and treatment options. Both physician and patient should be aware of the shortcomings of diagnostic tests
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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 | 0.016 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".