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Record W2337398034

Quality improvement of reporting standards for ablative studies: where do we stand?

2012· article· en· W2337398034 on OpenAlexaboutno aff
Ithaar Derweesh

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAblative caseHealth scienceFamily medicineMedical educationInternal medicineRadiation therapy
DOInot available

Abstract

fetched live from OpenAlex

© The Canadian Journal of UrologyTM; 19(5); October 2012 Address correspondence to Dr. Ithaar H. Derweesh, Moores UCSD Cancer Center, 3855 Health Sciences Drive, Mail Code: 0987, La Jolla, CA 92093 USA Quality improvement of reporting standards for ablative studies: where do we stand? Ithaar H. Derweesh, MD Division of Urology/Department of Surgery, University of California San Diego School of Medicine, La Jolla, California, USA

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)0.796
metaresearch head score (Gemma)0.826
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.204
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7960.826
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0120.007
Bibliometrics0.0180.020
Science and technology studies0.0080.015
Scholarly communication0.0260.029
Open science0.0150.012
Research integrity0.0150.030
Insufficient payload (model declined to judge)0.0030.002

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 head0.141
GPT teacher head0.388
Teacher spread0.247 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainReporting
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

Citations1
Published2012
Admission routes1
Has abstractyes

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