Annotation
Corrigendum to 'Machine-learning survival models for predicting time to recurrence in epithelial ovarian cancer' [Gynecologic Oncology 204 (2026) 184-193]
Gynecologic oncology, v 207, pp 48-49
04 Mar 2026
PMID: 41785532
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The authors regret an error in the labelling of subplots in Fig. 2 where the titles of subplots (b) and (c) are reversed. The upper-right subplot shows the significant coefficients of the Full TTR, High Stage PenCoxPH model while the lower-left subplot shows those for the Short TTR, All Stage model. The discussion regarding Fig. 2 in the body of the paper is unchanged.
The authors apologise for any inconvenience caused.
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Details
- Title
- Corrigendum to 'Machine-learning survival models for predicting time to recurrence in epithelial ovarian cancer' [Gynecologic Oncology 204 (2026) 184-193]
- Creators
- John Nakayama - Allegheny Health NetworkMichael McGaughey - Highmark Blue Cross Blue ShieldGrace Pindzola - Drexel University, College of Medicine, Philadelphia, PA, USAEirwen Miller - Allegheny Health NetworkThomas Krivak - UPMC Hillman Cancer CenterChristopher Morse - Allegheny Health NetworkSarah Crafton - Allegheny Health NetworkAlyssa Wield - Allegheny Health NetworkJeffrey Toole - Highmark Blue Cross Blue ShieldTiffany Summerscales - Highmark Blue Cross Blue Shield
- Publication Details
- Gynecologic oncology, v 207, pp 48-49
- Publisher
- Academic Press Inc
- Number of pages
- 2
- Resource Type
- Annotation
- Language
- English
- Academic Unit
- College of Medicine
- Web of Science ID
- WOS:001710204600001
- Scopus ID
- 2-s2.0-105031732651
- Other Identifier
- 991022165638804721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Obstetrics & Gynecology
- Oncology