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Record W2233923212

Surgery for osteitis pubis.

2006· article· en· W2233923212 on OpenAlexaff
Ramin Mehin, Robert Meek, Peter O’Brien, Piotr A. Blachut

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineSurgeryPubic symphysisPerineumSymphysisOsteitisThighAbdomenPelvisOsteomyelitis
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Osteitis pubis is a rare and self-limited condition. Surgery may be necessary in 5%-10% of cases. The outcome after surgery for osteitis pubis is not known. METHODS: To determine the success of surgical intervention for osteitis pubis, we used a computerized data registry to identify patients (10 women [mean age 40 yr]) who underwent surgery for osteitis pubis. A retrospective chart review was carried out. We also searched the literature for all cases of osteitis pubis managed surgically and identified 73 cases. RESULTS: The 10 patients in our series had had symptoms for a mean of 4 years preoperatively. Onset of pain was insidious in 4 patients, it followed childbirth in 4 and it followed trauma in 2. Depending on the surgeon's preference, either a wedge resection of the symphysis pubis was performed or a symphysiodesis. At the latest follow-up (average 26 mo), although all patients had some improvement, only 6 of 10 patients were satisfied with the outcome. From the literature review, we identified 3 categories of patients with osteitis pubis: elite athletes, patients with postoperative or infectious osteitis pubis and the remainder, which would include the patients in our series. CONCLUSIONS: Four types of surgical intervention are described: curettage, arthrodesis, wedge resection and wide resection. The elite athletes respond well to curettage. Patients with osteitis pubis following urologic or gynecologic procedures or have a proven infection require surgery in roughly 50% of cases. The third group has an unpredictable outcome.

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)0.000
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 score0.632
Threshold uncertainty score0.180

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.021
GPT teacher head0.231
Teacher spread0.210 · 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

Citations56
Published2006
Admission routes1
Has abstractyes

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