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Record W2518348989 · doi:10.12968/ijpn.2016.22.8.380

Clinical prediction survival of advanced cancer patients by palliative care: a multi-site study

2016· article· en· W2518348989 on OpenAlexaff
Vincent Thai, Sunita Ghosh, Yoko Tarumi, Gary Wolch, Konrad Fassbender, Francis Lau, Ingrid DeKock, Mehrnoush Mirosseini, Hue Quan, Ju Yang, Patrick Mayo

Bibliographic record

VenueInternational Journal of Palliative Nursing · 2016
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsTranslational Research in OncologyGrey Nuns Community HospitalCovenant HealthRoyal Alexandra HospitalUniversity of VictoriaUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsMedicinePalliative careCancerIntensive care medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

AIMS: This study examined (1) accuracy of clinician prediction of survival (CPS) by palliative practitioners on first assessment with the use of standardised palliative tools, (2) factors affecting accuracy, (3) potential impact on clinical care. METHODS: A multi-site prospective study (n=1530) was used. CPS was divided into four time periods (<=2wks, >2 to 6wks, >6 to 12wks and >12wks). Multivariate analysis was assessed on six predictor variables. RESULTS: Overall, median survival of the sample was only 5 weeks. CPS category was accurate only 38.6% of the time, with 44.6% patients dying before the predicted time period. Of six candidate variables, on multivariate analysis only (i) the clinical time periods themselves and (ii) Palliative Performance Scale <=50 predicted for prognostic accuracy. CONCLUSION: CPS, even by palliative practitioners, remains overly optimistic with the existence of the horizon effect. This raises the question in that these individuals may have been potentially overtreated.

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)0.001
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 score0.041
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.139
GPT teacher head0.511
Teacher spread0.372 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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