In Reply: Neurological Surgery Manpower Training and Density in Islamic Republic of Iran: A Population Study
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,077 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,015 | 0,020 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».