MétaCan
Menu
Back to cohort
Record W2094989037 · doi:10.1100/tsw.2009.24

Anatomic Considerations for Radical Retropubic Prostatectomy in an Achondroplastic Dwarf

2009· article· en· W2094989037 on OpenAlexaff
Dennis Gyomber, David Angus, Nathan Lawrentschuk

Bibliographic record

VenueThe Scientific World JOURNAL · 2009
Typearticle
Languageen
FieldMedicine
TopicUrological Disorders and Treatments
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineRadical retropubic prostatectomyAchondroplasiaProstatectomyProstate cancerRadiologySurgeryCancerInternal medicine

Abstract

fetched live from OpenAlex

This is the first report of a radical retropubic prostatectomy (RRP) in an achondroplastic dwarf. We highlight the pelvic anatomy, precluding laparoscopic or robotic prostatectomy, and making open surgery extremely difficult. We review relevant literature regarding general, urological, and orthopedic abnormalities of achondroplasia (ACH) and present a clinical case. No reports of RRP in achondroplastic dwarfs exist, with only one case of an abandoned RRP due to similar pelvic anatomy in a patient with osteogenesis imperfecta. Significant lumbar lordosis found in ACH results in a short anteroposterior dimension, severely limiting access to the prostate. We present a case of a 62-year-old achondroplastic dwarf who had Gleason 3+4 disease on transrectal ultrasound-guided biopsy in four from 12 cores. Surgery was difficult due to narrow anteroposterior pelvic dimension, but achievable. Histological analysis revealed multifocal prostate cancer, with negative surgical margins and no extraprostatic extension. RRP in ACH patients, although possible, should be approached with caution due to the abnormal pelvic dimensions, and discussions regarding potential abandonment of surgery should be included during informed consent. This case highlights the preoperative use of computed tomography to assist in the surgical planning for patients with difficult pelvic anatomy.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.339
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.311
Teacher spread0.275 · 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 teacher head, 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

Citations3
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueThe Scientific World JOURNALSame topicUrological Disorders and TreatmentsFrench-language works237,207