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Record W1969631216 · doi:10.1177/155335060501200216

Measuring Quality of Life After Surgery

2005· review· en· W1969631216 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSurgical Innovation · 2005
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsToronto General HospitalCancer Care OntarioMinistry of Health and Long Term Care
Fundersnot available
KeywordsQuality of life (healthcare)MedicineReliability (semiconductor)Measure (data warehouse)Quality (philosophy)Health careGerontologyNursingData miningComputer science

Abstract

fetched live from OpenAlex

Measures of quality of life are used increasingly to evaluate the outcome of surgical care. Impairment in quality of life is a major reason why patients seek surgical care, and changes in health-related quality of life are how patients assess the effect of treatment. Disease-specific measures focus on a particular health condition and are useful for detecting change resulting from treatment. Generic measures cover a wider spectrum of quality of life, provide a global assessment of a patient's overall health, and allow comparisons with other health conditions. Quality of life is not measured directly but is commonly sampled by using measurement scales in the form of questionnaires. The important properties of quality-of-life measurement scales are reliability, the extent to which a measure provides similar values for individuals with similar underlying quality of life; validity, the extent to which it measures what it purports to measure; responsiveness, the extent to which changes in correlate with true changes in quality of life; and sensitivity, the extent to which a measure can detect meaningful changes in quality of life.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.258
GPT teacher head0.407
Teacher spread0.149 · 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