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Record W2478316402

Medical Student Summer Clinical Externship in PM&R: a student’s experience

2016· article· en· W2478316402 on OpenAlexaffvenue
Alvin Ip

Bibliographic record

VenueHealth professional student journal · 2016
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpecialtyMedical schoolMedical educationMedicineRehabilitationPsychologyFamily medicinePhysical therapy
DOInot available

Abstract

fetched live from OpenAlex

Physical medicine and rehabilitation (PM&R), also known as physiatry, is a medical specialty that is lesser-known to medical students. One reason that medical students have a lack of knowledge about PM&R may be due to its limited exposure during medical school. The dual purposes of this elective report are to increase student exposure to PM&R and to highlight a clinical training opportunity for medical students. PM&R is a medical specialty concerned with the diagnosis and treatment of patients with neurological and musculoskeletal conditions, with a focus on restoring function and quality of life. The Medical Student Summer Clinical Externship (MSSCE) is a program offered by the Association of Academic Physiatrists for medical students with a strong desire to work with patients in the field of PM&R. I took part in the MSSCE at the University of Pittsburgh Medical Center in the summer of 2014. Participating in this program and gaining clinical exposure to PM&R was an important and valuable stepping-stone for me, and I would highly recommend the MSSCE to medical students who are interested in the field of PM&R.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0030.003
Open science0.0010.008
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.002

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 head0.086
GPT teacher head0.555
Teacher spread0.469 · 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 designQualitative
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
Published2016
Admission routes2
Has abstractyes

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