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Factors associated with use of medications for anxiety and depression in testicular cancer survivors after cisplatin-based chemotherapy.

2021· article· en· W3172027519 on OpenAlexaff
Shirin Ardeshir‐Rouhani‐Fard, Paul C. Dinh, Patrick O. Monahan, Sophie D. Fosså, Robert Huddart, Chunkit Fung, Yiqing Song, Darren R. Feldman, Robert J. Hamilton, David J. Vaughn, Neil E. Martin, Christian Kollmannsberger, Lifang Hou, Lawrence H. Einhorn, Kurt Kroenke, Lois B. Travis

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsPrincess Margaret Cancer CentreUniversity of British ColumbiaUniversity Health Network
FundersNational Institutes of Health
KeywordsMedicineAnxietyCommon Terminology Criteria for Adverse EventsDepression (economics)Testicular cancerInternal medicineQuality of life (healthcare)Adverse effectCancerPsychiatry

Abstract

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5025 Background: Cancer survivors are at increased risk of anxiety and depression that can affect health-related quality of life. There is no study to date that has examined the characteristics of testicular cancer survivors (TCS) taking medications for anxiety or depression since pharmacological interventions are typically reserved for more severe cases of these disorders. In this study, we aimed to examine sociodemographic factors, cisplatin-related adverse health outcomes (AHOs), and cumulative burden of morbidity (CBMPt) scores associated with medication use for anxiety and/or depression in TCS. Methods: A total of 1,802 TCS who completed CBCT ≥12 months previously completed validated questionnaires regarding sociodemographic features and cisplatin-related AHOs (hearing impairment, tinnitus, peripheral sensory neuropathy (PSN), kidney disease). Patients were recognized as users of medications for anxiety and/or depression if they used pharmacological classes of these medications and also indicated that the reason for use was for anxiety or depression. Individual AHOs were graded 0-to-4 based on severity according to NCI Common Terminology Criteria for Adverse Events version 4.03. A CBMPt score encompassed the number and severity of cisplatin-related AHOs. Multivariable logistic regression models assessed the relationship of individual AHOs and CBMPt with medication use for anxiety and/or depression. Results: A total of 151 TCS (8.4%) used medications for anxiety and/or depression. Any grade of HL, tinnitus, PSN, and kidney disease were reported by 37.9%, 39.5%, 55.2%, and 2.4% of 1,802 participants, respectively. No cisplatin-related AHO were reported by 511 (28.4%) participants, whereas 622 (34.5%), 334 (18.5%), 287 (15.9%), and 48 (2.7%), respectively, had very low, low, medium, and high CBMPt scores. Higher CBMPt scores were significantly associated with greater medication use for anxiety and/or depression (CBMPt scores of low (OR = 2.96, 95%CI, 1.67-5.24), medium (OR = 3.47, 95%CI, 1.95-6.18), and high (OR = 3.18, 95%CI, 1.22-8.3). A multivariable model including individual AHOs indicated that tinnitus ( P= 0.0009), PSN ( P= 0.02), and having health insurance (OR = 2.15, 95%CI, 1.01-4.56) were associated with significantly greater use of these medications; whereas being employed (OR = 0.39, 95%CI, 0.23-0.66) and vigorous physical activity (OR = 0.63, 95%CI,0.44-0.89) were associated with significantly diminished use. Conclusions: We found that TCS with higher CBMPt scores had a higher probability of using medications for anxiety and/or depression and conversely, those who were employed and physically active tended to have reduced use. These findings deserve further investigation in longitudinal studies. In the interim, healthcare providers should be aware of these associations in formulating survivorship care plans.

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.177
GPT teacher head0.485
Teacher spread0.308 · 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".

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Citations2
Published2021
Admission routes1
Has abstractyes

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