Bibliographic record
Abstract
Cancer in the developing world, of which the Islamic world is a substantial component, is characterized by far more advanced stages at diagnosis, fewer allocated resources for prevention and treatment, and higher incidence than in countries with more developed health systems.1 The top five cancers in the emerging world are (in descending order) stomach, lung, liver, breast, and cervix, and in developed countries the most common cancers are those of the lung, colorectum, breast, stomach, and prostate.2 In Indonesia, which has an estimated total cancer incidence of about 300,000 cases per year, only 10% are seen in the health care system.3 Similarly, only one cancer unit is available for about 120 million people in Bangladesh.4 Because preventive and curative services for cancer control are underdeveloped in many Islamic countries, the development of palliative care services is a more realistic option for most patients in these countries who have cancer. The available health care services in the Islamic world clearly do not meet patients' needs, and there is little sign that this situation will improve in the foreseeable future. Even if palliative care development is placed on an Islamic country's health care agenda, such development might be handicapped by technical and economic constraints. However, despite this gloomy picture, there are signs that palliative medicine is beginning to take off in the Islamic world. For example, the medical use of morphine for cancer pain control has been steadily increasing during the past few years in many Islamic countries.5 Once a palliative care program takes root in an Islamic country, it usually grows into a thriving service.3,6,7,8,9,10
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".