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Record W4376115440 · doi:10.3332/ecancer.2023.1548

Highlights from the Second Choosing Wisely Africa conference: a roadmap to value-based cancer care in East Africa (9–10 February 2023, Dar es Salaam, Tanzania)

2023· article· en· W4376115440 on OpenAlexaff
Eulade Rugengamanzi, Godwin Nnko, Fidel Rubagumya, Jerry Ndumbalo, Nazima Dharsee, Larry Akoko, Christian Ntizimira, Beda Likonda, Harrison Chuwa, Salum J. Lidenge, Verna Vanderpuye, Nazik Hammad, Sikudhani Muya

Bibliographic record

Venueecancermedicalscience · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsQueen's University
Fundersnot available
KeywordsTanzaniaMedicineRadiation oncologyDar es salaamClinical OncologyGynecologic oncologyPalliative careFamily medicineOncologyMedical educationInternal medicineCancerNursingRadiation therapySocioeconomics

Abstract

fetched live from OpenAlex

The ecancer Choosing Wisely conference was held for the second time in Africa in Dar es Salaam, Tanzania, from the 9th to 10th of February 2023. ecancer in collaboration with the Tanzania Oncology Society organised this conference which was attended by more than 150 local and international delegates. During the 2 days of the conference, more than ten speakers from different specialties in the field of oncology gave insights into Choosing Wisely in oncology. Topics from all fields linked to cancer care such as radiation oncology, medical oncology, prevention, oncological surgery, palliative care, patient advocacy, pathology, radiology, clinical trials, research and training were presented to share and bring awareness to professionals in oncology, on how to choose wisely in their approach to their daily practice, based on the available resources, while trying to offer the maximum benefit to the patient. This report, therefore, shares the highlights of this conference.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.004

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 head0.225
GPT teacher head0.389
Teacher spread0.164 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2023
Admission routes1
Has abstractyes

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