Suicidal Ideation in Newly-Diagnosed Chinese Cancer Patients
Bibliographic record
Abstract
OBJECTIVE: Suicide is one of the main reasons cited behind the death rate of cancer, and suicidal ideation (SI) is the first step toward it. This study aimed to investigate the prevalence and associates of suicidal ideation in newly-diagnosed Chinese cancer patients. METHODS: This multicenter study was conducted from January 2018 to September 2019. Eligible participants were asked to complete a Case Record Form (CRF), the Patient Health Questionnaire (PHQ-9), General Anxiety Disorder Questionnaire (GAD-7), Fear of Cancer Recurrence Questionnaire (FCRQ-7), and McGill Pain Questionnaire-Visual Analogue Scale (MPQ-VAS). Univariate analyses and multivariate logistic regression analyses were conducted for assessment. RESULTS: Out of 603 patients, 91 (15.1%, 95%CI: 12.23%-17.96%) reported suicidal ideation in the last 2 weeks. Physical comorbidities (OR=1.808, P=0.039), childhood adversity experience (OR=5.999, P=0.001), cancer pain (OR=1.828, P=0.047), depression (OR=2.811, P=0.013), and anxiety (OR=6.532, P<0.001) were significantly associated with suicidal ideation. It was also found that patients who regularly exercised were less likely to report suicidal thoughts (OR=0.172, P=0.007). CONCLUSION: Physical comorbidities, body ache, and mood disturbances are possible risk factors for suicidal ideation that warrant further attention in clinical practice. Preventive measures, such as systematic screening and arrangement for regular check-ups, could be beneficial to lower the risk of suicide.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".