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Record W4290999321 · doi:10.3389/fnins.2022.953842

Tumor markers and depression scores are predictive of non-suicidal self-injury behaviors among adolescents with depressive disorder: A retrospective study

2022· article· en· W4290999321 on OpenAlexaboutno aff
Pengcheng Yi, Yan-hua Qin, Chunmei Zheng, Keming Ren, Lei Huang, Wei Chen

Bibliographic record

VenueFrontiers in Neuroscience · 2022
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
FundersZhejiang UniversitySir Run Run Shaw Hospital
KeywordsLogistic regressionDepression (economics)Receiver operating characteristicClinical psychologyAnxietyInternal medicineMedicineGeneralized anxiety disorderRating scaleCancerMajor depressive disorderPsychologyOncologyPsychiatryMood

Abstract

fetched live from OpenAlex

Background: Non-suicidal self-injury (NSSI) is an important risk factor for suicide in adolescents with depressive disorders; therefore, it is important to predict NSSI occurrence as early as possible. Disturbances in biological rhythms are characteristic manifestations of depressive disorders and can lead to immune dysfunction, leading to changes in tumor markers. This study aimed to produce an index that utilizes tumor markers to predict NSSI behaviors among adolescents with depressive disorders. Methods: A total of 120 hospitalized adolescent patients with depressive disorders aged 14-24 years were included in this study. Participants were divided into NSSI and non-NSSI groups based on self-reports using the Ottawa Self-Injury Inventory. Demographics, tumor marker concentrations, other peripheral blood indices, Hamilton Depression Rating Scale (HDRS) scores, and Hamilton Anxiety Rating Scale (HAMA) scores were compared between the two groups. Logistic regression analysis was conducted to develop a joint index, and a receiver operating characteristic (ROC) curve was created to predict NSSI behaviors among adolescents with depressive disorders. Results: Compared with the non-NSSI group, the NSSI group had significantly higher insight, retardation, insomnia, hopelessness, psychiatric anxiety, total HDRS and HAMA scores, and significantly higher levels of cancer antigen 125 (CA-125), cancer antigen 19-9 (CA19-9), and carcinoembryonic antigen (CEA). In addition, a joint index was developed by combining CA-125, CA19-9, CEA, HDRS total score, HAMA total score and age using multiple logistic regression to predict NSSI behaviors. The area under the curve was 0.831, with a sensitivity and specificity of 0.734 and 0.891, respectively. Conclusion: A combination of depression score, tumor marker levels, and age can identify NSSI behaviors among adolescents with depressive disorders.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.257
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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