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Record W1967979861 · doi:10.1177/0272989x0202200309

Reduction Mammaplasty: Defining Medical Necessity

2002· article· en· W1967979861 on OpenAlexaboutno aff
Carolyn L. Kerrigan, E. Dale Collins, H. Myra Kim, Paul Schnur, Edwin G. Wilkins, Bruce L. Cunningham, Julie C. Lowery

Bibliographic record

VenueMedical Decision Making · 2002
Typearticle
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsnot available
Fundersnot available
KeywordsMammaplastyBreast reductionMedicineBreast surgeryPhysical therapyBreast cancerSet (abstract data type)Quality of life (healthcare)MEDLINESurgeryCancerComputer scienceNursing

Abstract

fetched live from OpenAlex

The authors evaluated existing and new criteria for defining the medical necessity for breast reduction surgery. Two cohorts of women (those requesting breast reduction surgery [N = 266] and a group of controls [N = 184]) completed a questionnaire including breast-specific symptom severity, the Short Form 36, the EuroQol, the McGill Pain Questionnaire, and the Multidimensional Body Self Relations Questionnaire. To evaluate prediction validity, the most widely accepted decision criteria and a new definition of medical necessity were applied to the data set to determine whether women meeting the definition had more favorable outcomes than those who did not as measured by validated self-report instruments. For existing criteria, women not meeting and meeting the criterion gained equal benefit from surgery. Women meeting the new definition (> or = 2 of 7 physical symptoms all or most of the time) had significantly greater improvement scores on 4 of the 5 health burden measures compared to women not meeting this definition. The authors conclude that medical necessity for breast reduction surgery is better defined by self-report of symptoms than by existing criteria.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.001

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 head0.026
GPT teacher head0.306
Teacher spread0.280 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations76
Published2002
Admission routes1
Has abstractyes

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