Authors' reply: Are low‐ and middle‐income countries achieving the Lancet commission global benchmark for surgical volumes? A systematic review
Notice bibliographique
Résumé
We thank Davis et al.1 for their interest and insightful feedback on our work about surgical volumes in low- and middle-income countries (LMICs).2 We appreciate Davis et al. for highlighting the importance of including nonacademic publications in the search strategy. The considerable increase in the number of countries reporting from 12 to 43 and surgeries from 877 to 1367 from these countries is expected due to the inclusion of data from ministry of health reports, national statistical databases, and gray literature from these countries. Our systematic review aimed to report and analyze surgical volume in LMICs, assess proxy indicators, and document the limitations and barriers to surgical volume data collection based on peer-reviewed scientific research.3 While we acknowledge that MOHs and national agencies often shoulder the responsibility of data collection in LMICs, there are major challenges in using this data for benchmarking or wider comparisons. Firstly, the bias in reporting, overreporting in particular, is a major challenge with this self-reported data. Secondly, not all countries report surgical volumes similarly, and denominators, methods and criteria for inclusion, and definitions of surgical procedures may vary considerably, affecting data comparability.4 Standardizing these diverse sources to ensure quality and reliability comparable to peer-reviewed publications is a challenge. Despite excluding gray literature, the heterogeneity in the collected data is a major barrier and challenge reported by our systematic review. Our study, hence, focused on peer-reviewed literature for methodological consistency and reliability. Also, the triangulation of data sources enhances understanding provided by self-reported data from government agencies. The discrepancy between academic publications and government reports underscores the need for harmonized data collection and reporting mechanisms. Standardizing data templates and incorporating them into existing government systems would pave the way for uniform definitions and reporting and would enhance the quality and availability of surgical data. This will also facilitate better monitoring and evaluation of progress in this important surgical indicator.5 In conclusion, we commend Davis et al. for their comprehensive review and strategy to collaborate with MOHs to improve data collection and reporting. Priti Patil: Conceptualization; data curation; formal analysis; writing – original draft; writing – review & editing. Priyansh Nathani: Conceptualization; data curation; formal analysis; writing – original draft; writing – review & editing. Juul M. Bakker: Conceptualization; formal analysis; writing – original draft; writing – review & editing. Alex J. van Duinen: Conceptualization; formal analysis; writing – review & editing. Pranav Bhushan: Writing – review & editing. Minal Shukla: Writing – review & editing. Samir Chalise: Writing – review & editing. Nobhojit Roy: Conceptualization; writing – review & editing. Anita Gadgil: Conceptualization; writing – review & editing. This research did not receive any external funding. The authors declare that they have no conflicts of interest.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,010 | 0,002 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».