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Record W2332317228 · doi:10.1186/s13256-016-0849-z

How to apply case reports in clinical practice using surrogate models via example of the trigeminocardiac reflex

2016· editorial· en· W2332317228 on OpenAlex
Nora Sandu, Tumul Chowdhury, Bernhard Schaller

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Medical Case Reports · 2016
Typeeditorial
Languageen
FieldMedicine
TopicTrigeminal Neuralgia and Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineClinical PracticeEngineering ethicsEpistemologyManagement scienceFamily medicineEngineering

Abstract

fetched live from OpenAlex

Case reports are an increasing source of evidence in clinical medicine. Until a few years ago, such case reports were emerged into systematic reviews and nowadays they are often fitted to the development of clinical (thinking) models. We describe this modern progress of knowledge creation by the example of the trigeminocardiac reflex that was first described in 1999 by a case series and was developed over the cause-and-effect relationship, triangulation to systematic reviews and finally to thinking models. Therefore, this editorial not only underlines the increasing and outstanding importance of (unique) case reports in current science, but also in current clinical decision-making and therefore also that of specific journals like the Journal of Medical Case Reports.

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)0.013
metaresearch head score (Gemma)0.048
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.227
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.048
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.003
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 head0.074
GPT teacher head0.423
Teacher spread0.349 · 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