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Record W2028055227 · doi:10.4236/wjcs.2014.43005

Effect of Surgical Complications on Quality of Life after Thoracoscopic Lobectomy for Lung Cancer

2014· article· en· W2028055227 on OpenAlexafffund
Sayf Gazala, Jeffrey Johnson, James D. Kutsogiannias, Eric L.R. Bédard

Bibliographic record

VenueWorld Journal of Cardiovascular Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsUniversity of Alberta
FundersAlberta InnovatesAlberta Innovates - Health SolutionsUniversity of AlbertaRoyal Alexandra Hospital Foundation
KeywordsMedicineQuality of life (healthcare)Lung cancerCohortVitalitySurgeryStage (stratigraphy)Cohort studyVideo-assisted thoracoscopic surgeryInternal medicine

Abstract

fetched live from OpenAlex

Background: Surgical resection is the main treatment for early stage lung cancer; the benefits of surgery, however, need to be weighed against possible complications and patients’ quality of life. Methods: We performed a cohort study following patients after video assisted thoracoscopic lobectomy at our tertiary care center. Before surgery, health related quality of life was assessed using the SF-36, the QLQ30, QLQ13 and EQ5D questionnaires. Post-operatively health related quality of life was assessed at regular intervals (2, 4, 8 and 12 weeks). A research team assessed post-operative complications on a daily basis during the patients’ hospital stay. Based on the Clavien classification system, the cohort was classified as experiencing high-grade (i.e., grade III or IV) complications or not. Changes in quality of life scores over the follow-up period were compared using linear regression with generalized estimating equations. Results: Between March and September 2011, 44 eligible patients were recruited into the study. The mean age was 65 (SD 8.7) years; 55% were male. The majority (n = 31; 71%) had no or low-grade complications. Patients experiencing high-grade complications reported significantly worse outcomes in the following domains of the SF-36: Global Health, Vitality, and Physical Functioning (p

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)0.003
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 score0.104
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.003
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 head0.019
GPT teacher head0.340
Teacher spread0.321 · 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 teacher head, 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

Citations0
Published2014
Admission routes2
Has abstractyes

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