MétaCan
Menu
Back to cohort

The Effect of Smoking on Clinical Outcome and Complication Rates Following Ilizarov Reconstruction

2003· article· en· W2057639775 on OpenAlexaff
Michael D. McKee, Dennis J. DiPasquale, Lisa M. Wild, David Stephen, Hans J. Kreder, Emil H. Schemitsch

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2003
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineComplicationSurgeryNonunionRetrospective cohort studyIncidence (geometry)

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the effect of smoking on outcome and complication rates following Ilizarov reconstruction. DESIGN: We performed a retrospective review of 84 adult patients (86 limbs) who underwent Ilizarov reconstruction. There were 39 "limbs" in nonsmokers and 47 "limbs" in active smokers. Complications and an outcome score based on ASAMI (Association for the Study and Application of the Methods of Ilizarov) criteria were recorded for each patient. DATA ANALYSIS AND RESULTS: There were 35 major complications including 15 malunions/nonunions, 7 refractures, 8 persisting infections, and 5 amputations. Results were measured using the ASAMI outcome scale. There were significantly more poor results in the smoking group than in the nonsmoking group (18/47, 38% versus 4/39, 10%; P = 0.003). Seven of eight patients with persisting infection were smokers (P = 0.049). There was a higher incidence of nonunion in the smoking group (P = 0.031). All five amputations were in smokers (P = 0.035). CONCLUSION: Smokers had a higher percentage of poor results (P = 0.01), due primarily to higher complication rates. Smoking is a significant, potentially remediable risk factor for failure following Ilizarov reconstruction, and cessation strategies are of paramount importance prior to initiating treatment.

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)0.001
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 score0.202
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.040
GPT teacher head0.370
Teacher spread0.330 · 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

Citations70
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Orthopaedic TraumaSame topicBone fractures and treatmentsFrench-language works237,207