Using a patient-generated mental-health measure 'PSYCHLOPS' to explore problems in patients with coronary heart disease
Bibliographic record
Abstract
Background Patients with coronary heart disease (CHD) who are depressed have an increased risk of further cardiac events and higher mortality.Aim To use a patient generated instrument (PSYCHLOPS) to define categories of concerns in patients with CHD. To define the psychometric characteristics of patients in each category.Design and setting Cross-sectional study set in general practices in south London.Method Of 3325 patients on the CHD registers in 15 general practices, 655 completed six baseline psychometric and functional instruments: PSYCHLOPS, HADS-Depression, HADS-Anxiety, Clinical Interview Schedule – Revised, SF12-Mental and SF12-Physical. Content analysis was used to categorise patients based on their main problem, as elicited by PSYCHLOPS. Mean psychometric scores were adjusted for confounding by age, sex, deprivation and ethnicity and calculated for each response category.Results Response categories were: physical problems, both non-cardiac (23.2%) and cardiac (6.0%); social problems: relationship/family (18.2%), money (7.5%), work (3.1%); functional (9.8%); psychological (6.9%); miscellaneous (7.3%); ‘no problem’ (18.2%). The highest psychological distress scores were found in ‘physical, cardiac’ and ‘psychological’ categories. The ‘no problem’ category had significantly lower psychological distress and higher functional capacity than other categories.Conclusions PSYCHLOPS enabled the identification of subtypes of CHD patients, based on a classification of self-reported problems. A high proportion of CHD patients report social problems. Psychological distress was highest in those reporting cardiac or psychological symptoms. Services should be aligned to the reported needs of patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".