Diabetes black spots and death by postcode
Bibliographic record
Abstract
Papers p 1389 Like the villain in Treasure Island , diabetes is well known for handing the “black spot” of early demise to its victims. This reputation will be enhanced by a study in this week's BMJ from South Tees, one of the United Kingdom's black spots for both poverty and premature death, mainly from cardiovascular disease. Roper et al present a depressing snapshot of the prospects for diabetic people in the UK today, which shows diabetes to be particularly mean: sexist, ageist, and with a clear tendency to kick the underdog (p 1389).1 Of their 4800 diabetic subjects, a quarter died during the study's six year span—an overall mortality about 2.2 times the national average. Those who developed diabetes youngest had their lives shortened the most: life expectancy was reduced by nine years for those diagnosed by the age of 40 but by only one year for those diagnosed at 80. Women diagnosed between 55 and 65 years of age lost two more years of life than did men. Finally, mortality tracked closely with socioeconomic deprivation, rising steadily from 1.3 times the national average in …
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".