Assessment of outcomes
Bibliographic record
Abstract
Effective healthcare delivery necessitates evaluation of the effect of interventions in the form of outcome assessment. Treatment effect includes measurement of how the patient feels, functions and survives following healthcare interventions. In haemophilia, which is a rare bleeding disorder, outcome assessment was characterized by a lack of validated outcome measurement tools and the challenges of hemophilia study design to collect outcome data. The aim of this communication is to share current thinking and, through practical examples, provide a state of the art practice in the assessment of hemophilia outcomes from a healthcare provider, patient/family and funder perspective. This discussion is timely and particularly relevant to the care of people with hemophilia on the eve of a number of novel hemophilia treatment products which are about to be licensed for use, specifically the long-acting factor VIII and factor IX concentrates. The first section by Dr Blanchet gives an overview of the tools currently available for assessment of structure/function, patient activities and patient participation in hemophilia healthcare delivery, pointing out the challenge of developing new tools and appropriate validation of currently available tools. The second section by Mr Brian O'Mahony emphasizes the essential collaboration and partnership between healthcare providers and people with hemophilia in collating the outcome data. In the third and final section, Mr Leigh McJames, gives a funder's perspective of the desirable outcomes of hemophilia care.
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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 | 0.037 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".