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Record W1983109948 · doi:10.1089/lap.2006.16.67

Interval Appendectomy: An Old New Operation

2006· article· en· W1983109948 on OpenAlexaffabout
Juan Bass, Steven Rubin, Abdulelah Hummadi

Bibliographic record

VenueJournal of Laparoendoscopic & Advanced Surgical Techniques · 2006
Typearticle
Languageen
FieldMedicine
TopicAppendicitis Diagnosis and Management
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineSurgeryLaparoscopyAbscessAppendicitisAcute appendicitisRetrospective cohort studyConfidence intervalAppendixGeneral surgeryInternal medicine

Abstract

fetched live from OpenAlex

A retrospective chart review of 32 patients who underwent laparoscopic interval appendectomy at the Children's Hospital of Eastern Ontario between May 1999 and December 2003 was performed. The age range was 0.5 to 18 years (mean, 11.8 years; median, 11.5 years). The interval time from the acute episode to the laparoscopic interval appendectomy ranged from 1 to 16 weeks (median, 6 weeks). The initial presentations were 11 patients with appendiceal abscess, 9 with appendiceal masses/phlegmons, and 12 patients with an acute but resolving clinical picture with ultrasonographic evidence of appendicitis. There were no wound infections or recurrent intra-abdominal abscesses. The average length of stay was 1.38 days, ranging from same-day discharge (1 patient) to a three-night stay (2 patients). There were no complications related to the laparoscopic technique, confirming reports that laparoscopic interval appendectomy is a technically safe procedure. Pathologic analysis of the appendices demonstrated acute or subacute changes in 14 patients (interval time = 7.9 weeks), chronic changes in 8 (interval time = 8.1 weeks), both acute and chronic changes in 5 (interval time = 8.2 weeks), and no pathologic diagnosis in 6 (interval time = 4.28 weeks). These findings support the need for interval appendectomy, and suggest that laparoscopy is a safe alternative to open appendectomy.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.313
Teacher spread0.300 · 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 designNot applicable
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

Citations16
Published2006
Admission routes2
Has abstractyes

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