MétaCan
Menu
Back to cohort
Record W2144660075 · doi:10.3109/09286586.2014.895843

Is it time to move beyond the QALY in vision research?

2014· editorial· en· W2144660075 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOphthalmic Epidemiology · 2014
Typeeditorial
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWillingness to payIncremental cost-effectiveness ratioQuality-adjusted life yearCost effectivenessEconomic evaluationIntervention (counseling)Unit (ring theory)Metric (unit)Cost–benefit analysisDiabetic retinopathyActuarial scienceOptometryFamily medicineOperations managementNursingDiabetes mellitusRisk analysis (engineering)

Abstract

fetched live from OpenAlex

Economic evaluation of health programs is the science of weighing the benefit of a health intervention against its cost. The metric used to evaluate this relationship is the incremental cost-effectiveness ratio (ICER), which represents the cost of each unit of effectiveness ‘‘purchased’’ through the health program. 1 For instance, if effectiveness of a medical treatment for diabetic retinopathy was measured in letters correct on an Early Treatment Diabetic Retinopathy Study (ETDRS) chart, an economic evaluation would compare the new intervention to the existing standard practice, estimating the difference in letters correct and the difference in cost. The cost-effectiveness of the intervention would be measured by dividing the difference in cost by the difference in letters correct, resulting in the ICER. The decision as to whether the intervention is ‘‘cost-effective’’ is based on comparing the ICER to the ‘‘willingness to pay’’ for that unit of effectiveness. If the ICER is less than the willingness to pay, the program would be adopted. 2 While this process is straightforward in theory, the problem in practice is that there are few payors who have established the willingness to pay for any unit of effectiveness – be it letters correct, lives saved, or cases screened. The notable exceptions to this are payors in Canada, the United Kingdom, and the European Union who established a standard willingness to pay for a quality-adjusted life year (QALY) gained through a health program. 3–5 The QALY is a composite measure in which a year lived is weighed by a value representing the quality of life enjoyed during that year. The metric used to estimate the quality of life associated with that year is a preference-based measure referred to as a utility. It is measured on a scale ranging from perfect health (1.0) to death (zero) 6 which, in theory, encompasses all possible health states. Therefore, a health-related utility elicited in this manner provides a common measure of effectiveness by which all health-related interventions might be judged and a standard willingness to pay for a QALY might be established. Estimating utilities in this fashion is a time consuming and cognitively difficult task for most study participants requiring that the respondent engage in a series of trade-offs in which they balance their desire for perfect health against a risk of death or

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.210
metaresearch head score (Gemma)0.189
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.332
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2100.189
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.103

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 head0.508
GPT teacher head0.564
Teacher spread0.056 · 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