The comprehensive care of sickle cell disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Millions of people across the world have sickle cell disease (SCD). Although the true prevalence of SCD in Europe is not certain, London (UK) alone had an estimated 9000 people with the disorder in 1997. People affected by SCD are best managed by a multidisciplinary team of professionals who deliver comprehensive care: a model of healthcare based on interaction of medical and non-medical services with the affected persons. The components of comprehensive care include patient/parent information, genetic counselling, social services, prevention of infections, dietary advice and supplementation, psychotherapy, renal and other specialist medical care, maternal and child health, orthopaedic and general surgery, pain control, physiotherapy, dental and eye care, drug dependency services and specialist sickle cell nursing. The traditional role of haematologists remains to co-ordinate overall management and liase with other specialities as necessary. Co-operation from the affected persons is indispensable to the delivery of comprehensive care. Working in partnership with the hospital or community health service administration and voluntary agencies enhances the success of the multidisciplinary team. Holistic care improves the quality of life of people affected by SCD, and reduces the number as well as length of hospital admissions. Disease-related morbidity is reduced by early detection and treatment of chronic complications. Comprehensive care promotes awareness of SCD among affected persons who are encouraged to take greater control of their own lives, and achieves better patient management than the solo efforts of any single group of professionals. This cost-effective model of care is an option for taking haemoglobinopathy services forward in the new millennium.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it