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Multinational Comparison of Prophylactic Antibiotic Use for Eyelid Surgery

2015· article· en· W2047428729 on OpenAlexaboutno aff
Aaron Fay, Nambi Nallasamy, Francesco P. Bernardini, Edward J. Wladis, Marlene L. Durand, Martín H. Devoto, Dale R. Meyer, Morris E. Hartstein, Santosh G Honavar, Midori H. Osaki, Tammy H. Osaki, Yvette Marie B. Santiago, Marco Sales‐Sanz, G. Vadalà, David H. Verity

Bibliographic record

VenueJAMA Ophthalmology · 2015
Typearticle
Languageen
FieldMedicine
TopicOcular Infections and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEyelidMedical prescriptionAntibioticsPerioperativeSurgeryPlastic surgery

Abstract

fetched live from OpenAlex

IMPORTANCE: Antibiotic stewardship is important in controlling resistance, adverse reactions, and cost. The literature regarding antibiotic use for eyelid surgery is lacking. OBJECTIVES: To determine standard care and assess factors influencing antibiotic prescribing practices for eyelid surgery. DESIGN, SETTING, AND PARTICIPANTS: A survey study was conducted from February 2, 2014, to March 24, 2014. The survey was distributed to 2397 oculoplastic surgeons in private and academic oculoplastic surgery practices in 43 countries. All surgeons were members of ophthalmic plastic and reconstructive surgery societies. Data were analyzed by geographic location. Linear regression was performed to quantify contributions to rates of prescribing postoperative antibiotics for routine eyelid surgical procedures. MAIN OUTCOMES AND MEASURES: Rates of prescribing prophylactic intravenous, oral, and topical antibiotics as well as factors that influence surgeons' prescribing practices. RESULTS: A total of 782 responses were received from 2397 surgeons (average response rate, 36.7%; 2.5% margin of error) from 43 countries. Topical antibiotic use was common in all regions (85.2%). Perioperative intravenous antibiotic use was uncommon in all regions (13.5%). Geographic location was the greatest predictor of antibiotic prescribing practices (range, 2.9% in the United Kingdom to 86.7% in India; mean, 24%). Within Europe, Italy had the highest rate of antibiotic prescriptions for eyelid surgery (41.7%) and the United Kingdom had the lowest rate (2.9%.) In South America, Venezuela had the highest rate of antibiotic prescriptions for eyelid surgery (83.3%) and Chile had the lowest rate (0%). The practice locations that were associated with routinely prescribing postoperative oral antibiotics were India (odds ratio [OR], 15.83; 95% CI, 4.85-51.68; P < .001), Venezuela (OR, 13.47; 95% CI, 1.43-127.19; P = .02), and Southeast Asia (OR, 2.80; 95% CI, 1.15-6.84; P = .02). Conversely, practice location in the United Kingdom (OR, 0.048; 95% CI, 0.0063-0.37; P = .004), Australia and New Zealand (OR, 0.15; 95% CI, 0.033-0.67; P = .01), and the United States and Canada (OR, 0.41; 95% CI, 0.23-0.72; P = .002) were associated with decreased rates of postoperative oral antibiotic use. Surgeons' concern for allergic reactions was associated with decreased rates of prescribing antibiotics (OR, 0.34; 95% CI, 0.23-0.49; P < .001), while surgeons' concern for infection was associated with increased rates of prescribing antibiotics (OR 1.80; 95% CI, 1.45-2.23; P < .001). CONCLUSIONS AND RELEVANCE: These results from members of ophthalmic plastic and reconstructive surgery societies confirm that antibiotic prescribing practices for routine eyelid surgical procedures vary widely throughout the world. No standard of care has been established that would require the routine use of postoperative prophylactic antibiotics following eyelid surgery.

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)0.007
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.162
GPT teacher head0.401
Teacher spread0.238 · 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 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".

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Citations33
Published2015
Admission routes1
Has abstractyes

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