The comprehensive care of sickle cell disease
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.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".