Awareness, Training, and Perceived Needs of Gynecologists in Interpreting Basic Imaging Studies: A Cross-Sectional Survey in Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Gynecologists often rely on imaging studies such as ultrasound, CT, and MRI for clinical decision-making, yet limited training in interpreting these studies may affect their confidence and ability to make timely diagnoses. This study aimed to assess the awareness, training and perceived needs of gynecologists in Saudi Arabia regarding the interpretation of basic imaging studies. METHODS: A cross-sectional survey was conducted among 200 gynecologists practicing in Saudi Arabia. Participants were recruited using convenience sampling, and data were collected through an online questionnaire that assessed demographics, imaging knowledge, training history, perceived barriers, and interest in further education. Descriptive statistics were used to analyze the data. RESULTS: The majority of respondents (44%) reported interpreting imaging studies occasionally, with ultrasound being the most commonly interpreted modality (63%). However, only 29% had received formal training in imaging studies, and 74% felt their training was insufficient. Most respondents (82%) expressed interest in additional training, particularly in ultrasound interpretation and emergency imaging. Key barriers to effective imaging interpretation included lack of training (43%) and reliance on radiology reports (24%). Nearly half (45%) of participants noted that delays in radiology reports affected their clinical decision-making. CONCLUSIONS: This study reveals significant gaps in imaging interpretation training among gynecologists in Saudi Arabia, with a high demand for further education in basic imaging modalities. Addressing these gaps through structured training programs could improve gynecologists' confidence and clinical decision-making, ultimately leading to better patient care outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".