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Record W4313389326 · doi:10.21089/njhs.63.0092

Consultants & Trainees Active Participation in Multi-Disciplinary Team (MDT): Tumor Boards can Play a Major Role in the Achievement of their Academic Professional Development Goals

2022· article· en· W4313389326 on OpenAlexaff
Fatima Shaukat, Muhammad Owais Aziz, Sehrish Abrar, Ahmed Nadeem Abbası, Tahir Shamsi

Bibliographic record

VenueNational Journal of Health Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsSt. Lawrence College
Fundersnot available
KeywordsMultidisciplinary approachCurriculumDisciplineMedical educationProfessional developmentMultidisciplinary teamSocioeconomic statusMedicineModernization theoryHealth careTeamworkPsychologyPedagogyNursingSociologyPolitical science

Abstract

fetched live from OpenAlex

We wish to share with the readers of this scientific journal our experiences and observations regarding educational gains achieved by the consultant faculty and trainee postgraduate residents via active participation in multidisciplinary team (MDT) Tumor Boards. The rapid modernization of teaching methodologies is being observed in both postgraduate residents and faculty professional education learning processes. Contemporary literature is increasingly depicting academic work which is being directed towards the exploration of both educational and social determinants of health care [1]. Several socio-economic factors have been discussed across the globe which have an impact on the health care systems [2]. Researchers argued over the fact that the leading factor for a positive outcome is socioeconomic wellbeing especially for patients with diseases, like cancer, diabetes mellitus, etc. If we critically evaluate the curriculums designed on the principles of traditional problem-based learning and case-based learning, they are very well-established students centre approaches, but on their merit, they failed to provide a holistic picture of the patient care [3].

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)0.011
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 score0.461
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.090
GPT teacher head0.466
Teacher spread0.376 · 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
Published2022
Admission routes1
Has abstractyes

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