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Record W1598459365 · doi:10.1111/jpc.12772

Benchmarking the management of children with haemophilia in <scp>A</scp>ustralia

2014· letter· en· W1598459365 on OpenAlexaboutno aff
Chris Barnes, Julie Curtin, Simon Brown, Ram Suppiah, Jamie Price, Susan Russell, Ritam Prasad, Michael Seldon

Bibliographic record

VenueJournal of Paediatrics and Child Health · 2014
Typeletter
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsnot available
Fundersnot available
KeywordsHaemophiliaMedicineClotting factorBenchmarkingHaemophilia APediatricsHaemophilia BInternal medicine

Abstract

fetched live from OpenAlex

Benchmarking can be used to identify best practice and establish a standard of performance1 and may be used in the health-care setting to optimise resource allocation while maximising clinical outcomes. Haemophilia A and haemophilia B are caused by congenital deficiency of factor VIII and factor IX, respectively, and may lead to recurrent, spontaneous bleeding into the muscles and joints.2 Management is available in the form of prophylactic infusions of clotting factor concentrates which prevent bleeding episodes and greatly improve the quality of life of these patients. Management of patients with haemophilia is complex and expensive with the majority of the cost directed towards funding clotting factor concentrate. Clinical management of haemophilia is co-ordinated by 16 haemophilia treatment centres (HTCs) based in metropolitan centres around Australia. In an attempt to identify the standard of care of children with haemophilia in Australia, a benchmarking initiative involving site visits to paediatric HTCs around Australia was conducted. Available literature on standards of care for haemophilia from the United States, Canada, United Kingdom and Europe was reviewed, and a data collection tool was developed. A reviewer visited the eight paediatric HTC responsible for managing children with haemophilia in Australia. At the site visits, a semi-structured interview was conducted, and an estimate of the time HTC staff committed to managing patients with haemophilia was assessed (Fig. 1 ). Most centres were able to provide the majority of core services considered essential for paediatric haemophilia care3 with significant differences in personnel resources. Haemophilia treatment guidelines recommend that physiotherapy and social work should have dedicated roles in a comprehensive haemophilia care team. Physiotherapy is critical in rehabilitation from acute musculoskeletal bleeds and promoting joint health independent of bleeding episodes. Social worker input can provide both psychosocial support and facilitate patient/carer access to essential community services and develop psychological strategies to improve coping with a chronic illness. In the absence of core team care services, patients may be less equipped to appropriately manage bleeding episodes and may rely instead on additional, potentially unnecessary and expensive clotting factor infusions. Alternate strategies with the use of physical rehabilitation and appropriate use of anti-inflammatory medications are an additional adjuvant to joint bleeding and pain.4 Providing expert assessment of joint function and psychological well-being is critical to ensure the cost effectiveness of clotting factor concentrate in patients with haemophilia is optimised. Relative EFT for individual haemophilia treatment centres for each core team member. , medical director; , nurse specialist; , physiotherapist; , social worker; , data manager.

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 imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.023
GPT teacher head0.293
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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