Selection of Neurosurgical Applicants in the High-Income Developing Country Lacking Local Residency Program: A Cross-Sectional Study
Bibliographic record
Abstract
Abstract Background Neurosurgery residency became one of the most competitive specialties in the medical field worldwide, which is increasing with time in contrast to the limited positions. Therefore, the requirements for the program have increased. There are different criteria for each program, which are determined by specific factors. It has become increasingly important for medical students to be aware of the factors that affect their acceptance into the program. There was a lack of data regarding the factors that contribute to the selection of neurosurgery residents in Oman Methods A questionnaire composed of 14 questions was conducted, using the SurveyMonkey Web site, among neurosurgeons in Oman which was distributed to the five hospitals that have neurosurgery departments in Oman. SPSS software was used in the analysis of the collected data. Results Forty-four participants responded to the survey. Ninety-five percent of them answered all the questions. Out of all participants, only two were female participants. Standardized international exam scores, such as the United State Medical Licensing Examination and Medical Council of Canada Qualifying Examination, ranked as the most important factor with a percentage of 44, followed by interview performance with a percentage of 33. While the least important factor was the age of applicants, which 46% of the participants ranked 8. Conclusion Most of the participants agreed that standardized exams are the most important factor in the selection of neurosurgery residents followed by interview performance, although there was no significant statistical difference between the two.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".