Evidence map›Paper›PMID 42207277›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

Tree-based and sparse logistic models for predicting one-month postoperative performance status after surgery for spinal metastases.

Satoshi Maki, Yuki Shiratani, Sumihisa Orita, Akinobu Suzuki, Koji Tamai, Takaki Shimizu, Kenichiro Kakutani, Yutaro Kanda, Hiroyuki Tominaga, Ichiro Kawamura and 45 more

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Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

55 authors.

Satoshi MakiChiba University, Chiba, Japan. satoshimaki@gmail.com.
Yuki ShirataniChiba University, Chiba, Japan.
Sumihisa OritaChiba University, Chiba, Japan.
Akinobu SuzukiOsaka Metropolitan University, Osaka, Japan.
Koji TamaiOsaka Metropolitan University, Osaka, Japan.
Takaki ShimizuKanazawa University, Kanazawa, Japan.
Kenichiro KakutaniKobe University, Kobe, Japan.
Yutaro KandaKobe University, Kobe, Japan.
Hiroyuki TominagaKagoshima University, Kagoshima, Japan.
Ichiro KawamuraKagoshima University, Kagoshima, Japan.
Masayuki IshiharaKansai Medical University Hospital, Osaka, Japan.
Masaaki PakuKansai Medical University Hospital, Osaka, Japan.
Yohei TakahashiKeio University, Tokyo, Japan.
Toru FunayamaUniversity of Tsukuba, Tsukuba, Japan.
Kousei MiuraUniversity of Tsukuba, Tsukuba, Japan.
Eiki ShirasawaKitasato University, Sagamihara, Japan.
Hirokazu InoueJichi Medical University Hospital, Tochigi, Japan.
Atsushi KimuraJichi Medical University, Shimotsuke, Japan.
Takuya IimuraDokkyo Medical University, Mibu, Japan.
Hiroshi MoridairaDokkyo Medical University, Mibu, Japan.
Hideaki NakajimaUniversity of Fukui, Fukui-shi, Japan.
Shuji WatanabeUniversity of Fukui, Fukui-shi, Japan.
Koji AkedaMie University, Tsu, Japan.
Norihiko TakegamiMie University, Tsu, Japan.
Kazuo NakanishiKawasaki Medical School, Kurashiki, Japan.
Hirokatsu SawadaNihon University, Tokyo, Japan.
Koji MatsumotoNihon University, Tokyo, Japan.
Masahiro FunabaYamaguchi University, Yamaguchi, Japan.
Hidenori SuzukiYamaguchi University, Yamaguchi, Japan.
Haruki FunaoInternational University of Health and Welfare Narita Hospital, Chiba, Japan.
Tsutomu OshigiriSapporo Medical University, Sapporo, Japan.
Takashi HiraiInstitute of Science Tokyo, Tokyo, Japan.
Bungo OtsukiKyoto University, Kyoto, Japan.
Kazu KobayakawaKyushu University, Fukuoka, Japan.
Koji UotaniOkayama University Hospital, Okayama, Japan.
Hiroaki ManabeTokushima University, Tokushima, Japan.
Shinji TanishimaTottori University, Yonago, Japan.
Ko HashimotoTohoku University, Sendai, Japan.
Chizuo IwaiGifu University Hospital, Gifu City, Japan.
Daisuke YamabeIwate Medical University, Morioka, Japan.
Akihiko HiyamaTokai University, Tokyo, Japan.
Shoji SekiUniversity of Toyama, Toyama, Japan.
Kenji KatoNagoya City University, Nagoya, Japan.
Masashi MiyazakiOita University, Ōita, Japan.
Kazuyuki WatanabeFukushima Medical University, Fukushima, Japan.
Toshio NakamaeHiroshima University, Hiroshima, Japan.
Takashi KaitoOsaka University, Osaka, Japan.
Hiroaki NakashimaNagoya University, Nagoya, Japan.
Narihito NagoshiKeio University, Tokyo, Japan.
Gen InoueKitasato University, Sagamihara, Japan.
Shiro ImagamaNagoya University, Nagoya, Japan.
Kota WatanabeKeio University, Tokyo, Japan.
Satoshi KatoKanazawa University, Kanazawa, Japan.
Seiji OhtoriChiba University, Chiba, Japan.
Takeo FuruyaChiba University, Chiba, Japan.

Funding

Japan Society for the Promotion of Science JP24K12366
6 · The paper itself

Abstract

purposeWe aimed to develop and internally validate prediction models for one-month postoperative performance status (PS) after surgery for spinal metastases and to identify patients likely to achieve PS 0-2 at one month.

methodsWe performed a retrospective analysis of a prospectively collected spine surgery registry. We compared three tree-based models (Random Forest, XGBoost, and CatBoost) with two regularized logistic regression models (ridge-regularized logistic regression and a sparse elastic-net logistic regression model constrained to ≤ 15 predictors). Model development and hyperparameter tuning were performed using nested cross-validation. Missing data were handled using model-specific strategies within the cross-validation pipeline, and a sensitivity analysis excluded the predictor with the highest missingness. Performance was assessed using discrimination and calibration metrics, including the area under the receiver operating characteristic curve (AUC-ROC), accuracy, precision, recall, F1 score, Brier score, calibration intercept, and calibration slope.

resultsThe primary analysis included 375 patients with available one-month PS out of 413 enrolled patients. Random Forest achieved the highest discrimination (AUC-ROC 0.811 ± 0.079) and showed calibration measures closest to the ideal among the evaluated models (Brier score 0.168; calibration intercept - 0.024; slope 1.121). The sparse elastic-net model showed good discrimination (AUC-ROC 0.796 ± 0.081) with a limited set of predictors, although its calibration metrics suggested less reliable absolute probability estimates (Brier score 0.217; intercept 0.612; slope 3.228). Excluding the predictor with the highest missingness yielded similar performance for the main models.

conclusionTree-based models, particularly Random Forest, provided the most favorable overall predictive performance for one-month postoperative PS after surgery for spinal metastases, whereas a sparse elastic-net logistic regression model preserved reasonable discrimination with a small predictor set and coefficient-based interpretability. These findings support clinically oriented prediction of early postoperative functional status while highlighting the need to assess calibration before clinical implementation.

Indexed as

Spinal NeoplasmsAgedBoosting Machine Learning AlgorithmsClassification AlgorithmsFemaleHumansLogistic ModelsMaleMiddle AgedPostoperative PeriodPrediction AlgorithmsPredictive Learning ModelsRandom ForestRetrospective StudiesMachine learningMetastatic spinal tumorsPerformance statusPredictive model

Identifiers

PMID42207277

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.