MétaCan
Menu
Back to cohort
Record W1969570627 · doi:10.1186/1758-3284-1-25

Head & neck optical diagnostics: vision of the future of surgery

2009· article· en· W1969570627 on OpenAlexaff
Tahwinder Upile, Waseem Jerjes, Henricus J. C. M. Sterenborg, Adel K. El‐Naggar, Ann Sandison, Max J. H. Witjes, Merrill A. Biel, Irving J. Bigio, Brian J. F. Wong, Ann M. Gillenwater, Alexander J. MacRobert, Dominic J. Robinson, Christian Betz, Herbert Stepp, Lina Bolotine, Gordon McKenzie, Charles A. Mosse, Hugh Barr, Zhongping Chen, Kristian Berg, Anil D′Cruz, Nicholas Stone, Catherine Kendall, Sheila Fisher, Andreas Leunig, Malini Olivo, Rebecca Richards‐Kortum, Khee Chee Soo, Vanderlei Salvador Bagnato, Lin-Ping Choo-Smith, Katarina Svanberg, I. Bing Tan, Brian C. Wilson, Herbert C. Wolfsen, Arjun G. Yodh, Colin Hopper

Bibliographic record

VenueHead & Neck Oncology · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsUniversity of TorontoNational Research Council CanadaNational Research Council Institute for Biodiagnostics
FundersNational Institute for Health and Care Research
KeywordsMedicineHead and neck surgeryOtorhinolaryngologyHead and neckGeneral surgeryTranslational researchSurgeryMedical physicsPathology

Abstract

fetched live from OpenAlex

Review paper and Proceedings of the Inaugural Meeting of the Head and Neck Optical Diagnostics Society (HNODS) on March 14th 2009 at University College London. The aim of our research must be to provide breakthrough translational research which can be applied clinically in the immediate rather than the near future. We are fortunate that this is indeed a possibility and may fundamentally change current clinical and surgical practice to improve our patients' lives.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0040.005
Open science0.0010.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0200.005

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 head0.033
GPT teacher head0.325
Teacher spread0.292 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations40
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueHead & Neck OncologySame topicHealthcare Systems and TechnologyFrench-language works237,207