Reporting of mortality in a psoriatic arthritis clinic is primarily a function of the number of clinic contacts and not disease severity.
Bibliographic record
Abstract
OBJECTIVE: To identify processes that influence data collection, particularly in the reporting of deaths in mortality studies, using patient registry data. METHODS: The University of Toronto Psoriatic Arthritis Clinic has mechanisms for patient followup and identification of deaths. Logistic regression was used to identify patient characteristics that discriminate between 2 populations of deaths, those reported under regular followup and those reported in the context of special studies. Factors examined were based on information available at the patients' last clinic visit and the pattern of patients' clinic visits. RESULTS: A clear relationship was found between the number of contacts with the clinic and rapid death reporting. However, no particular link between severity of disease and the reporting of death was apparent in this study. CONCLUSION: It is recommended that research databases routinely record the time between death and reporting of death and the method of ascertaining and reporting death. More detailed information on the scheduling of clinic visits may also be helpful.
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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 | 0.030 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".