In Reply: Neurological Surgery Manpower Training and Density in Islamic Republic of Iran: A Population Study
Bibliographic record
Abstract
To the Editor: We, the investigators of the submission: Neurological Surgery Manpower Training and Density in Islamic Republic of Iran: A Population Study,1 appreciate the letter submitted.2 We will try to answer all the questions and ambiguities point-by-point and to the best of our knowledge considering our Key Question (KQ). KQ: How Iran, a Low Middle-Income Country, was able to train 1200 neurosurgeons (1.4/100 000 population) within a 72-year period? What sociopolitical factors played a major role in this success? Two typographical errors are fully accepted. Orumieh is the capital of West Azerbaijan, Tabriz the capital of East Azerbaijan, and Shiraz the capital of Fars (Table 1 page 5, lines 4 and 8, and page 7, paragraph 1).1 Following the policy of decentralization of neurosurgical training by the Ministry of Health and Medical Education and approval by the Iranian Board of Neurosurgery, on 12 May, 2024, Zanjan, the capital of Zanjan Province, was given the opportunity to prepare the foundations of residency training for one of 6 years under strict supervision of the Tehran University of Medical Sciences. As such, 5 of those 6 years will be spent in one of the residency training programs in Tehran. This decision was made after our research project was initiated, and the submission was completed. The question of neurosurgical malpractice was not included in our KQs, and we consider expressing opinion as irrelevant. Neurosurgery residents are accepted after a nationwide entrance examination and the exact policy of distribution is centrally determined and we did not investigate the process. We consider the question irrelevant to this investigation. Medical and surgical management of trauma victims including traumatic brain injury and spinal cord injury across the country is very much similar to Shiraz, and this was rechecked by our direct contact with the authorities handling the trauma care. As such nearly all of the 31 provinces of the Iranian mainland are covered by air transportation. Since 1965, the World Federation of Neurosurgical Societies and Iranian Society of Neurosurgery hand in hand together have been active in the education of neurosurgical residents and fellows, facilitating participation in International Conferences, fellowship training programs (hands on or as an observer), and educational workshops. “Stringent US sanctions” has a political connotation and has no relevance to our study; however, sanctions have not blocked the abovementioned educational activities of the Iranian Society of Neurosurgeons under the auspices of World Federation of Neurosurgical Societies and purchase of neurosurgical equipment and instruments. Application of Clinical Practice Guidelines is well received in Iran, it is free of charge, and is based on published recommendations (PubMed, OVID database, Scopus database, Cochrane Library etc.); in addition, discussion of Advanced Trauma Life Support guidelines is not relevant to this study.3-6
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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 | 0.009 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".