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Record W1996111914 · doi:10.3747/co.19.1069

The Importance of Multidisciplinary Team Management of Patients with Non-Small-Cell Lung Cancer

2012· article· en· W1996111914 on OpenAlexaffvenue
Peter Ellis

Bibliographic record

VenueCurrent Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsMcMaster UniversityJuravinski Cancer Centre
Fundersnot available
KeywordsMultidisciplinary approachPsychosocialMedicineMultidisciplinary teamQuality of life (healthcare)Palliative careLung cancerIntensive care medicinePsychosocial supportNursingOncologyPsychiatry

Abstract

fetched live from OpenAlex

Historically, a simple approach to the treatment of non-small-cell lung cancer (nsclc) was applicable to nearly all patients. Recently, a more complex treatment algorithm has emerged, driven by both pathologic and molecular phenotype. This increasing complexity underscores the importance of a multidisciplinary team approach to the diagnosis, treatment, and supportive care of patients with nsclc. A team approach to management is important at all points: from diagnosis, through treatment, to end-of-life care. It also needs to be patient-centred and must involve the patient in decision-making concerning treatment. Multidisciplinary case conferencing is becoming an integral part of care. Early integration of palliative care into the team approach appears to contribute significantly to quality of life and potentially extends overall survival for these patients. Supportive approaches, including psychosocial and nutrition support, should be routinely incorporated into the team approach. Challenges to the implementation of multidisciplinary care require institutional commitment and support.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.217

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 head0.032
GPT teacher head0.402
Teacher spread0.370 · 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

Citations54
Published2012
Admission routes2
Has abstractyes

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