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Record W6982563064

Innovations in Trauma Training With Henry Heimlich, M.D. (1997)

2016· other· en· W6982563064 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDigitalGeorgetown (Georgetown University Library) · 2016
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)Test (biology)Trauma careHealth careEmergency medical servicesContinuing educationCurriculum
DOInot available

Abstract

fetched live from OpenAlex

The Maryland Institute for Emergency Medical Services Systems (MIEMSS) in Baltimore has developed an innovative program using life-like simulators and human cadavers to teach the life- saving skills taught in trauma training classes offered by many medical centers in the U.S. and Canada. These courses, which train physicians to provide emergency care to trauma patients, often use live animal laboratories to demonstrate procedures and test students. The MIEMSS program is an alternative to the use of live animals in training. Henry Heimlich, M.D. narrates this examination of one program using alternatives to train physicians in trauma care. The film includes interviews with the course director, a course instructor and the participants who reflect on their experience with the program. The film is designed for course instructors and for health professionals interested in options that reduce or eliminate the use of animals in trauma training.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.023
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0050.004
Science and technology studies0.0000.001
Scholarly communication0.0000.004
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.006

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 head0.014
GPT teacher head0.195
Teacher spread0.181 · 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