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Use of the Derriford Appearance Scale 59 to assess patient-reported outcomes in secondary cleft surgery

2016· article· en· W2435641780 on OpenAlexaffabout
Sophie Ricketts, Eran Regev, Oleh Antonyshyn

Bibliographic record

VenuePlastic Surgery · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineRhinoplastyQuality of life (healthcare)Patient-reported outcomePopulationSurgeryPhysical therapyNoseNursing

Abstract

fetched live from OpenAlex

P rimary cleft lip repair often incorporates correction of the cleft noseto reduce the psychosocial consequences of an uncorrected facial difference; however, it does not always eliminate the need for future secondary surgery (1).It is this aspect of cleft lip and/or palate (CLP) facial difference that is most likely to require secondary surgery in early adulthood.The purpose of this surgery is to achieve a more 'normal' nasal appearance by addressing the asymmetry and the stigma associated with CLP.Goals of this surgery are sometimes functional (airway) but are also significantly aesthetic.It alters the individual's facial appearance, and this changes the way in which they perceive themselves, the way they are perceived by others and, therefore, impacts psychological and social well-being (2,3).The method of evaluation of the outcomes of this surgery, with significant aesthetic goals, deserves consideration.Assessment of treatment interventions or surgery has conventionally been performed by physicians.This outcome assessment may include photographic analysis, anatomical measurements and complications.These measures, although important, do not capture the patient's satisfaction -their perception of the result or their healthrelated quality of life (QoL).The patient perspective is particularly relevant in facial reconstructive surgery where the impact on patients' QoL is complex and not necessarily reflected in objective physicianinterpreted outcomes (4,5).Assessing the perspective of a patient with CLP in relation to function, aesthetics and psychosocial well-being should be paramount in measuring surgical outcomes.Patient-reported outcomes (PROs) are concepts that are important to patients with different health conditions.PROs can be measured using purposely designed questionnaires that measure the patient's perspective without interpretation from anyone else.Such measures can capture concepts, such as health-related QoL (a multidomain concept with physical, psychological and social components), and can be used to provide evidence of a treatment benefit from the patient's perspective (6).Many PRO instruments have been used in plastic surgery; however, for a PRO instrument to be scientifically sound it should assess the impact of surgical intervention in a clinically meaningful manner (7).The majority of measures in plastic surgery have not been formally developed in a standardized manner (ie, they have not been tested for reliability [ability to produce consistent scores], validity [ability to measure what is intended to be measured] or responsiveness [ability to measure change]).Reviews of the literature have revealed the lack of a cleft-specific PRO instrument to assess QoL in CLP patients (8,9).In clinical practice, given the lack of a PRO instrument for patients with CLP, generic instruments, or non-disease-specific tools such as the Pediatric Quality of Life Inventory and Child Health Questionnaire have been used as a substitute (10,11).

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)0.003
metaresearch head score (Gemma)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.280
Teacher spread0.227 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

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