Neurological Surgery Manpower Training and Density in Islamic Republic of Iran: A Population Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Significant disparities in worldwide neurosurgical training and workforce distribution are prominent primarily in low-income and middle-income countries. Although Iran is considered a lower middle-income country, neurosurgical density and distribution in Iran has surpassed the recommended ratio of 1 neurosurgeon for every 100 000 population. The objective was to determine neurological surgery density and distribution in Iran and the factors significant in the relative success in training and allocation of neurosurgeons in Iran. METHODS: Review of PubMed and administration of site surveys of multiple data sources including Neurosurgical Society of Iran, Iranian Board of Neurological Surgery, Medical Council of Islamic Republic of Iran, Universities of Medical Sciences in Iran, and Ministry of Health and Higher Education of Iran. RESULTS: Over the 72-year period from 1952 to 2024, 1200 neurosurgeons have been trained and distributed in 31 provinces in Iran, attaining a ratio of 1.4/100 000 population. All but 40 neurosurgeons were trained after 1981, which coincided with the Iran-Iraq War. Decentralization of medical and neurosurgical residency training programs, resolving the immediate need for neurosurgeons managing penetrating traumatic brain and spinal cord injuries during the 1980 to 1988 Iran-Iraq War, and active participation of legislative and executive branches of government in solving health care disparities were major factors in meeting the needs of the country. At the present time, more than 555 neurosurgeons are practicing in Tehran Province, a proportion of 3.8 neurosurgeons for every 100 000 population, which indicates an element of disparity in density distribution across Iranian land. CONCLUSION: Legislative initiatives and government support of public health care delivery and decentralization of medical and residency training programs after the Iran-Iraq War and introduction of the Ministry of Health and Medical Education are considered the main reasons for the relative success in meeting the neurosurgical demand and manpower density. Still, further adjustment of distribution of manpower is needed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".