Bibliographic record
Abstract
In 2002 theWorld Health Organization reported that chronic conditions accounted for 46% of the global burden of disease (World Health Organization, 2002).While chronic conditions have always been part of the human experience, advances in medicine and biomedical technologies have extended the expected lifespan for many chronic diseases and have transformed a number of previously fatal illnesses, such as HIV infection, into chronic conditions.An increasingly toxic nanomaterial environment may well be implicated in the proliferation of newer chronic conditions, such as fibromyalgia, as well as increased prevalence rates for well-established conditions, such as asthma.Thus, we now have more chronic conditions and are recognizing them more often. As a result of theWHO report, health-care systems across theWestern world have been scrambling to respond to the increasing awareness of the pervasiveness of chronic disease, by enacting service delivery and process reform.This has created a new appreciation for the extent to which our health-care systems are built upon an acute-care ideology that all but ignores the plight of the chronically ill.To the extent that they are precursors of devastating and costly health problems, chronic diseases must be recognized as appropriate and cost-effective points of intervention and support.There is clear evidence that our health-care systems can no longer afford their predilection for tertiary care innovation as the solution to society’s health problems.Thus we are witnessing a renewed enthusiasm for a complete revision of health-service delivery.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".