Brain Damage Treated with Non Proven Intensive Training 2003-2011: A Norwegian Cost Analysis
Bibliographic record
Abstract
OBJECTIVES: There has been an increased request for intensive training and rehabilitation of patients with brain damage in Norway. These programs are demanding with regard to personnel, travelling, time and economic resources. We aimed to indicate cost and gain to make these programs cost-effective. METHODS: A retrospective study included all patients referred to the Northern Norway Regional Health Authority (NNRHA) trust during the nine years period 2003-2011. All referrals to the NNRHA trust for the economic coverage of foreign based rehabilitation or habilitation programs (The Advanced Bio-Mechanical Rehabilitation (ABR), Institutes of Achievement of Human Potential program (IAHP) (Doman Method), Family Hope Center (FHC) program and the Kozijavkin Method) were included. 17 patients were detected and 15 fulfilled the inclusion criteria for funding. Median age was 8 years (1-31 years). Cost from the specialist health care point of view was calculated. A cut-off limit of €57,000/quality adjusted life year (QALY) and a 4% discount rate was employed. RESULTS: The undiscounted cost per patient enrolled was calculated €133,210 (discounted €121,348). To make these therapies cost effective, a total of at least 2.13 QALYs (2.34 undiscounted QALYs) must be gained per patient enrolled. Such a gain could not be indicated and we doubt it is achievable. CONCLUSION: Non-proven intensive training programs for patients with brain damage are costly. As long as their effect has not been documented, health care services should not spend resources on these programs outside clinical trials.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".