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Record W2910275397 · doi:10.21037/jtd.2019.01.02

Patient-reported outcomes in lung and esophageal cancer

2019· review· en· W2910275397 on OpenAlexaff
Dhruvin H. Hirpara, Vaibhav Gupta, Lisa M. Brown, Biniam Kidane

Bibliographic record

VenueJournal of Thoracic Disease · 2019
Typereview
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsHealth Sciences CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineLung cancerEsophageal cancerGeneral surgeryCancerIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Carcinomas of the lung and esophagus are associated with significant disease and treatment related morbidity. Measuring patients' self-perceived notion of their health-related quality of life (HRQOL), throughout the course of illness, is central to the delivery of comprehensive, patient-centered care. This article reviews commonly used HRQOL instruments in thoracic surgery and discusses the integral role of patient-reported outcomes (PROs) in comparative effectiveness research and prognostication in the realm of lung and esophageal cancer. We also highlight challenges and future directions for widespread implementation of PROs into clinical and research practice.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.860
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.454
Teacher spread0.397 · 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 designOther design
Domainnot available
GenreReview

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

Citations18
Published2019
Admission routes1
Has abstractyes

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