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Record W2108971488 · doi:10.3233/jrs-2002-274

Experiences with patient charges

2002· article· en· W2108971488 on OpenAlexaboutno aff
Flora M. Haaijer‐Ruskamp

Bibliographic record

VenueInternational Journal of Risk & Safety in Medicine · 2002
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsAccounts payableMedical prescriptionPaymentBusinessActuarial scienceFinanceMedicine

Abstract

fetched live from OpenAlex

Pharmaceutical policies generally provide for patients to pay a part of the costs of the drugs which they receive. A variety of co-payment systems exist. As noted in Chapter 3, they can take the form of (1) a fixed sum per prescription; (2) a percentage of the overall cost of the prescription; (3) a combination of fixed sums and percentages; and (4) deductibles. In OECD countries the system of fixed sums is used less then the proportional system, i.e. a percentage of the prescription price. Fixed sums are payable in Australia, Austria, Germany, Japan, New Zealand and the UK, while percentage charges apply in Belgium, Canada, Denmark, France, Greece, Hungary, Ireland, Korea, Luxembourg, Norway, Portugal, Spain, Sweden, Switzerland, Turkey and under private insurance schemes in the USA. Belgium and Italy employ mixed systems. The proportion of co-payment required varies in some countries with the therapeutic value of the drug. In many low and middle-income countries co-payment has been introduced since the 1980’s as an element in cost recovery schemes or revolving funds in which patients are asked user fees (see Chapter 15).

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)0.118
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.118
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.004
Science and technology studies0.0090.004
Scholarly communication0.0080.006
Open science0.0030.009
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0410.003

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.

Opus teacher head0.284
GPT teacher head0.527
Teacher spread0.243 · 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".

Quick stats

Citations6
Published2002
Admission routes1
Has abstractyes

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