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Record W1963939161 · doi:10.1002/bjs.5428

Non-patient factors related to rates of ruptured appendicitis

2007· article· en· W1963939161 on OpenAlexafffund
Nadine Sicard, Pierre Tousignant, Raynald Pineault, S. S. Dube

Bibliographic record

VenueBritish journal of surgery · 2007
Typearticle
Languageen
FieldMedicine
TopicAppendicitis Diagnosis and Management
Canadian institutionsMcGill UniversityInstitut National de Santé Publique du QuébecHôpital Maisonneuve-RosemontUniversité de Montréal
FundersUniversité de MontréalCanadian Health Services Research FoundationMcGill University
KeywordsMedicineAppendicitisGeneral surgery

Abstract

fetched live from OpenAlex

BACKGROUND: Adult rates of ruptured appendicitis vary from 13.2 to 41.9 per cent in urban hospitals, despite controlling for individual factors. This suggests an effect of hospital organization. Surgeons report that appendicectomies may be delayed because of lack of access to operating rooms. METHODS: Combining interviews with hospital personnel and information from medical records for 1998-1999, a cross-sectional study using logistic regression, taking hospital clustering of patients into account, was conducted on 861 patients from 12 hospitals. Hospitals were grouped into organizational models. The diagnostic information was recoded to ensure interhospital validity. RESULTS: Hospitals with high activity and volumes of patients, but without an operating room designated for urgent surgery, were associated with a significantly higher risk of peritonitis (P<0.050). Time to surgery was very long in all hospitals, particularly time after departure from the emergency department and for elderly patients. CONCLUSION: Organizational characteristics, in unfavourable combinations, influence the course of time-dependent diseases such as appendicitis. Difficulties in gaining access to operating rooms, even for urgent operations, have emerged. Delays in treatment must be addressed when planning healthcare reforms.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.025
GPT teacher head0.285
Teacher spread0.260 · 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

Citations35
Published2007
Admission routes2
Has abstractyes

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