Obstetric Ultrasonography in Postgraduate Radiology Training
Bibliographic record
Abstract
ABSTRACT: There is no study in the literature that reveals the adequacy of obstetric ultrasonography (US) training in Turkey. We aimed to evaluate the adequacy of obstetric US training radiologists had received during their residency and determine how competent they considered themselves to be in this regard.A survey (27 items for residents, 21 items for specialists) was sent to all the radiology residents and specialists in Turkey through the mail list of the Turkish Society of Radiology.Ninety-one residents and 217 specialists participated in our study. Sixteen residents (17.6%) had received theoretical courses, 21 residents (23.1%) and 59 specialists (27.2%) had attended in-house obstetric US rotations, and 5 residents (5.5%) and 23 specialists (10.6%) had attended obstetric US rotations in another institution. When questioned separately for each trimester, only 11% to 36.3% of the residents stated that they took care of a sufficient number of patients. In general, 62.6% of the residents and 25.3% of the specialists did not consider themselves to be absolutely competent in obstetric US. The competency sources were specified as residency training by 44 residents (48.6%) and 55 specialists (25.3%), postgraduate training by 2 residents (2.2%) and 78 specialist (35.9%).In Turkey, current obstetric US training does not provide the experience that will allow physicians with radiology training to easily perform and interpret obstetric US. The main reasons for this situation include the limited number of patients the physicians took care of as a resident, insufficient rotation time, and lack of theoretical courses they attended.
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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 | 0.002 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".