Recording of clinical information in a Scotland-wide drug deaths study
Bibliographic record
Abstract
The aim of this study was to analyse the nature and extent of data extracted from case files of deceased individuals in contact with services 6 months prior to drug deaths in Scotland during 2003. A cross-sectional descriptive analysis of 317 case notes of 237 individuals who had drug-related deaths was undertaken, using a data linkage process. All contacts made with services in the 6 months prior to death were identified. Information on clinical and social circumstances obtained from social care, specialist drug treatment, mental health, non-statutory services, the Scottish Prison Service and Criminal Records Office was collated. More than 70% (n = 237) were seen 6 months prior to their drug death. Sociodemographic details were reported much more frequently than medical problems, for example, ethnicity (49%), living accommodation (66%), education and income (52%) and dependent children (73%). Medical and psychiatric history was recorded in only 12%, blood-borne viral status in 17% and life events in 26%. This paucity of information was a feature of treatment plans and progress recorded. The 237 drug deaths were not a population unknown to services. Highly relevant data were missing. Improved training to promote in-depth recording and effective monitoring may result in better understanding and reduction of drug deaths.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".