Undocumented alcoholism and its correlation with tobacco and illegal drug use in advanced cancer patients
Bibliographic record
Abstract
BACKGROUND: The objectives of this retrospective study were to determine the frequency of undiagnosed alcoholism among patients with advanced cancer who were referred to palliative care and to explore its correlation with alcoholism, tobacco abuse, and use of illegal drugs. METHODS: The authors reviewed 665 consecutive charts and identified 598 patients (90%) who completed a screening survey that was designed to identify alcoholism, the Cut Down, Annoyed, Guilty, Eye Opener (CAGE) questionnaire, including 100 consecutive patients who had CAGE-positive and CAGE-negative results. Data on tobacco and illegal drug use, the Edmonton Symptom Assessment Scale, and the morphine equivalent daily dose were collected. RESULTS: The frequency of CAGE-positive results in this palliative care population was 100 of 598 patients (17%). Only 13 of 100 patients (13%) in that CAGE-positive group had been identified as alcoholics before their palliative care consultation. Compared with CAGE-negative patients, CAGE-positive patients were younger (aged 58.6 years vs 61.3 years; P = .07), predominantly men (68 of 100 patients vs 51 of 100 patients; P = .021), more likely to have a history of tobacco use (86 of 100 patients vs 48 of 100 patients; P < .001), more likely to be actively using nicotine (33 of 100 patients vs 9 of 100 patients; P = .02), and more likely to have a history of illegal recreational drug use (17 of 100 patients vs 1 of 100 patients; P < .001). Pain and dyspnea were worse in patients who had a history of nicotine use. Both CAGE-positive patients and patients who had a history of tobacco use more frequently were receiving strong opioids at the time of their palliative care consultation. CONCLUSIONS: The current findings suggested that alcoholism is highly prevalent and frequently under diagnosed in patients with advanced cancer. CAGE-positive patients were more likely to have a history of, or to actively engage in, smoking and illegal recreational drug use, placing them at risk for inappropriate opioid escalation and abuse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".