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Record W3023894570 · doi:10.1016/j.jmpt.2006.12.003

[no title]

2007· article· en· W3023894570 on OpenAlexaff
Cynthia Peterson

Bibliographic record

VenueJournal of Manipulative and Physiological Therapeutics · 2007
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsCanadian Memorial Chiropractic College
Fundersnot available
KeywordsMedicineQuality (philosophy)ROWEInclusion (mineral)Medical physicsRadiologyEpistemologyManagement

Abstract

fetched live from OpenAlex

Nearly every chiropractor in the world and many others from the medical profession are familiar with the previous editions of Yochum and Rowe's Essentials of Skeletal Radiology because their radiology content, image quality, and inclusion of extremely valuable clinical information in an easy-to-read and concise format were second to none. What could these authors possibly do to improve on the successes of the first two editions? The third edition, which came out in 2005, includes 1000 new references, incorporating a vast quantity of new information constantly arising in the field of diagnostic imaging. It also adds 500 new illustrations, all of which are of outstanding quality and size to make even the novice understand the concepts presented.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.752
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2480.185

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 head0.266
GPT teacher head0.406
Teacher spread0.140 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2007
Admission routes1
Has abstractyes

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