Integrating GPT-4o Into Data Mining in Neurosurgery: Feasibility and Proof-of-Concept Study
Notice bibliographique
Résumé
Background: Large language models offer new possibilities for transforming unstructured clinical text into structured datasets. However, their performance in specialized and complex documentation environments, such as neurosurgery, remains insufficiently characterized. GPT-4o is a large language model with enhanced natural language capabilities, but its accuracy in extracting structured data from neurosurgical reports has not been systematically assessed. Objective: This proof-of-concept study evaluated the feasibility and accuracy of GPT-4o for extracting predefined structured variables from unstructured neurosurgical reports of patients with vestibular schwannoma. Specific aims were to measure accuracy across variable types, assess the impact of prompt refinement, and explore the model's potential utility for research-oriented data mining. Methods: In this retrospective single-center study, 10 consecutive patients with histologically confirmed vestibular schwannoma who underwent surgery between August and December 2023 were included. Four anonymized German-language documents per patient (discharge, surgical, histopathology, and 3-month follow-up reports) were processed using GPT-4o. Seventeen variables were extracted using a standardized zero-shot prompt. Targeted prompt refinements were subsequently applied for variables with low baseline accuracy. Two board-certified neurosurgeons independently validated all outputs, with discrepancies resolved by a senior neurosurgeon. Accuracy metrics, 95% CIs (Wilson method), and descriptive comparisons between variable types were calculated. Results: GPT-4o achieved 100% accuracy for structured variables requiring minimal interpretation, including patient ID, date of birth, date of surgery, histopathological diagnosis, and World Health Organization grade. Several interpretative variables, such as symptoms at presentation, symptom type, symptom duration, extent of resection, and permanence of postoperative deficits, were also extracted with 100% accuracy. In contrast, intraoperative complications and new postoperative deficits were correctly identified in only 50% (5/10) of cases using the zero-shot prompt. After targeted prompt refinement, accuracy for these variables improved substantially, reaching 90% to 100% in most cases. The mean accuracy was highest for structured categorical variables (97.5%, SD 4.6%), intermediate for binary variables (80%, SD 27.4%), and lowest for conditional text variables (66.7%, SD 28.9%), without statistically significant differences (P=.25). Conclusions: GPT-4o demonstrated strong feasibility for structured data extraction from standardized neurosurgical reports, particularly for variables with limited semantic complexity. However, the high accuracy observed reflects a narrow and highly controlled context and should not be interpreted as evidence of general reliability across diverse clinical settings. Larger, multi-institutional, and multilingual studies are needed to determine broader applicability and potential clinical integration.
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 | 0,012 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».