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Record W2799780747 · doi:10.1097/md.0000000000010519

Rare foreign body in bladder

2018· article· en· W2799780747 on OpenAlexaff
Yubing Li, Yunqiu Gao, Xiangdong Chen, Shaobo Jiang

Bibliographic record

VenueMedicine · 2018
Typearticle
Languageen
FieldMedicine
TopicForeign Body Medical Cases
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsMedicineForeign bodyMEDLINEUrinary bladderUrologySurgery

Abstract

fetched live from OpenAlex

RATIONALE: The bladder is the most common site of foreign bodies in the urinary tract. Presenting complaints in patients with a foreign body are urinary retention, dysuria, frequent urination, decreased urine volume, nocturia, hematuria, painful erection, as well as pain in the urethra and pelvis. PATIENT CONCERNS: A 50-year-old married male presented with complaints of severe lower abdominal pain and dysuria. DIAGNOSES: A plain radiograph of the pelvis showed a metallic dense foreign body that was composed of many small magnetic balls in the pelvic region. INTERVENTIONS: The foreign body was removed under cystoscopy, and 67 magnetic balls were detected without any surgical or postsurgical complications. OUTCOMES: During operation, A cystoscopic examination confirmed no residue. LESSONS: The bladder is the most common site of a foreign body in the urinary tract.Most intravesical foreign bodies can be removed transurethrally and with minimum access. The best mode of management depends on the nature of the foreign body, lodged site, expertise of the surgeon, and available instruments.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.027
GPT teacher head0.313
Teacher spread0.286 · 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 designCase report
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

Citations18
Published2018
Admission routes1
Has abstractyes

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