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Record W2150731445 · doi:10.1258/002221502761698748

Computer imaging and patient satisfaction in rhinoplasty surgery

2002· article· en· W2150731445 on OpenAlex
Henry Sharp, R. S. Tingay, Scott Coman, V. A. MILLS, David Roberts

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

VenueThe Journal of Laryngology & Otology · 2002
Typearticle
Languageen
FieldMedicine
TopicNasal Surgery and Airway Studies
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsRhinoplastyPatient satisfactionMedicineAuditIntervention (counseling)SurgeryMedical imagingMedical physicsRadiologyNoseNursing

Abstract

fetched live from OpenAlex

The measurement and achievement of improved patient benefit following a particular medical or surgical intervention has become an increasingly relevant part of the provision of effective healthcare. We have retrospectively analysed patient satisfaction in 56 patients following rhinoplasty via the Glasgow Benefit Inventory (GBI), 25 of whom underwent pre-operative computer imaging planning. We have also audited patient reaction to this technique via a concurrent questionnaire in those subjects who underwent imaging, and correlated this with overall patient outcome. Patient satisfaction with cosmetic rhinoplasty following computer imaging was significantly improved compared to those patients who did not receive imaging.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.231
Teacher spread0.216 · 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