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Will development of human anatomy revolutionize medical education?

2020· article· en· W3016890551 on OpenAlexaboutno aff
Rajani Singh

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumMedical educationMedicineMedical knowledgeAccreditationGross anatomyAnatomyPsychology

Abstract

fetched live from OpenAlex

The growing trend of vanishing cadaveric dissection, pruning of anatomical curriculum, compression of teaching schedule of Anatomy, deployment of inappropriately qualified tutors and demonstrators and removal of expert and experienced anatomical faculty with closing of Anatomy department all over the world in general and USA, Canada, UK, Australia and New Zealand in particular due to commercialization of medical education has not only eroded standard of medical education but also accelerated the failure cases evidenced by rising medico‐legal cases. So there is worldwide clamor about inadequate knowledge of Anatomy among tomorrows’ doctors causing impediments to comprehend successful clinical practice in general and pathology, surgery, imagery and anestheology in particular. Although many anatomic and clinical stalwarts proposed ways and means to reform delivery/acquisition of Anatomy but hunger of earning chunk of profit from medical institutions never let the quality of medical education improve. Therefore, a new model of Anatomy, to strengthen delivery and acquisition of Anatomy for revolutionizing medical education, has been designed. If adopted divorcing greed of making money from medical schools to save holy and sacred quality medical education from ruining further, the medical profession in general and pathology, surgery, imagery and anestheology in particular will regain their lost glory. The literature was reviewed and factors, continuously contributing to falling standard and growing failure cases due to insufficient knowledge of Anatomy in trainees effecting clinical practice in medical education, were analysed. The analysis evolved model for development of Anatomy as a subject to root out these deficiencies by standardizing curriculum to be taught by medically qualified and experienced faculties with strong background of beautifully blended standalone and collaborative research in variant macro/microanatomy arresting failure cases and litigations. In addition to this, the model will create an environment for easy and necessary interaction of anatomists with clinicians/trainees through providing instant anatomical solution at all levels of medical education during clinical training of medical trainees directly from well guided cadaveric dissection by experienced faculty. The model will also generate readymade records in form of table or charts containing the variant macro/microanatomical information corresponding to relevant clinical complications to help clinicians/clinical trainees during clinical practice. This model will not only provide future reservoir of competent anatomical faculty but also revolutionize clinical skill tremendously among medical trainees.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.005
Scholarly communication0.0050.009
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.011
GPT teacher head0.258
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations3
Published2020
Admission routes1
Has abstractyes

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