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Record W2012769844 · doi:10.1109/tbme.2013.2239644

Motion of the Kidney Between Preoperative and Intraoperative Positioning

2013· article· en· W2012769844 on OpenAlexafffund
Caitlin Schneider, Christopher Nguan, M. Longpre, Robert Rohling, Septimiu E. Salcudean

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsVancouver General HospitalUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupine positionNephrectomyFlankMedicinePosition (finance)Prone positionRadiologySurgeryKidneyNuclear medicineAnatomyInternal medicine

Abstract

fetched live from OpenAlex

For many laparoscopic surgical procedures, the preoperative images are taken with the patient in a different position than that in which the surgery is performed. The organ shift between positions can affect surgical image guidance, as the organ shifts can complicate image registration. In particular, for partial nephrectomy, the standard clinical approach requires supine preoperative computed tomography, while the surgery is performed in the flank position. We studied ten subjects in both supine and flank positions. Rigid registration was used to determine the relative motion of the kidneys, using the spine as a pose-independent landmark. Our results showed that the kidney can move as much as 46.5 mm as a result of a supine-to-flank change in patient position, and rotate as much as 25°. From the results, significant kidney motion occurs due to the change of patient position from supine to flank. These changes warrant further study to understand and model the patient specific motion.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.178
Teacher spread0.173 · 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 designObservational
Domainnot available
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

Citations21
Published2013
Admission routes2
Has abstractyes

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