Assessing Patient-Reported Symptoms to Predict Postoperative Complications from Pituitary Surgery Using a Novel Consensus-Developed Inventory
Notice bibliographique
Résumé
Background: Endoscopic endonasal pituitary surgery is a safe and effective procedure that can often be discharged on the first or second post operative day. Published data suggests up to 8 to 10% of patients undergoing pituitary surgery can develop complications which require unscheduled visits, readmission, or reoperation, often in the 7 to 14 days following surgery prior to their first postoperative visit. During this time of increased risk, healthcare providers must utilize careful questioning and timely communication, often via phone or patient portals to determine if a patient is at risk of impending complications. We posit that patient reported symptoms can be utilized to build a predictive algorithm and reveal a patient's estimated risk following endoscopic endonasal pituitary surgery. Thus we utilized a validated expert consensus strategy to build a novel inventory to assess patient-report symptoms collected remotely following surgery. Methods: A modified Delphi technique was utilized to establish consensus amongst six rhinologists with high-volume endoscopic skull base surgery practices on the questions posed in the patient-reported symptom inventory. Experts initially submitted potential questions within the seven most common complication domains: “endocrinopathies,” “cerebrospinal fluid (CSF) leak,” “epistaxis,” “pain,” “infection,” “ophthalmologic/visual symptoms,” and “other.” Four rounds of voting discussion were required to achieve consensus on all questions, defined as at least five of six panel members agreeing to approve or remove a question from the final questionnaire. After each round, there was a discussion session conducted remotely. The final discussion occurred in-person. Results: Initially, 61 questions assessing patient-reported symptoms were proposed by and sent to experts for anonymous ranked voting. Agreement on the inclusion, content, and specific phrasing of each question was achieved via the ranked voting process and virtual/in-person discussion. Duplicate questions were eliminated prior to the first round of voting. After round 1, seven questions reached consensus for inclusion. In round 2, five questions reached consensus for inclusion. Round 3 was performed via an online meeting, and nine more questions reached consensus for inclusion. Finally, round 4 was performed via in-person discussion and eight questions reached consensus for inclusion. In total, 17 questions were included, with the remainder excluded by consensus due to similarity to other included questions, or because of other identified deficiencies. The distribution of included questions in the seven complication domains includes endocrine (4), CSF leak (3), epistaxis (2), pain control (2), postoperative infection (2), ophthalmologic (1), and other (3). Conclusion: Utilizing the modified Delphi consensus technique, our group developed a 17-question patient-reported symptom inventory which will be utilized to collect post-operative data on patients undergoing endoscopic pituitary surgery to further understand and potentially mitigate complications in the early postoperative period. Publication History Article published online: 01 February 2023 © 2023. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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 | 0,006 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».