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Record W3044439770 · doi:10.1097/spc.0000000000000512

Schizophrenia and cancer

2020· review· en· W3044439770 on OpenAlexaff
Alexandre González-Rodríguez, Javier Labad, Mary V. Seeman

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2020
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicinePsychiatrySchizophrenia (object-oriented programming)Palliative carePopulationMental illnessQuality of life (healthcare)Mental healthMEDLINEHealth careStigma (botany)Nursing

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The cancer mortality rate in persons with schizophrenia is higher than it is in the general population. The purpose of this review is to determine why, and to identify solutions. RECENT FINDINGS: The recent literature points to three groups of reasons why mortality is high: patient reasons such as nonadherence to treatment, provider reasons such as diagnostic overshadowing, and health system reasons such as a relative lack of collaboration between medicine and psychiatry. Strategies for cancer prevention, early detection, and effective treatment are available but difficult to put into practice because of significant barriers to change, namely poverty, cognitive and volitional deficits, heightened stress, stigma, and side effects of antipsychotic medication. The literature makes recommendations about surmounting these barriers and also offers suggestions with respect to support and palliative care in advanced stages of cancer. Importantly, it offers examples of effective collaboration between mental health and cancer care specialists. SUMMARY: The high mortality rate from cancer in the schizophrenia population is a matter of urgent concern. Although reasons are identifiable, solutions remain difficult to implement. As we work toward solutions, quality palliative care at the end of life is required for patients with severe mental illness. VIDEO ABSTRACT.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.469
Teacher spread0.292 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations26
Published2020
Admission routes1
Has abstractyes

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