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Record W2334009221 · doi:10.1097/prs.0b013e31824eff0a

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2012· article· en· W2334009221 on OpenAlexaffabout
Toni Zhong, Stefan O. P. Hofer

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineConfidence intervalOdds ratioCategorical variableTable (database)Logistic regressionSurgeryAnesthesiaStatisticsInternal medicineMathematicsData mining

Abstract

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Sir:FigureWe thank Dr. Otiv for the Letter to the Editor regarding our article featured in the December issue of the Journal, entitled “Intravenous Fluid Infusion Rate in Microsurgical Breast Reconstruction: Important Lessons Learned from 354 Free Flaps” (Plast Reconstr Surg. 2011;128:1153–1160). Most of your queries are outlined in the multivariable analysis section in the article, including Table 3 and the plot in Figure 1; however, we understand that your questions were based on access to the abstract alone and unfortunately all of the information could not be conveyed in the abstract. Please refer to our responses to your queries below: Odds ratio (Table 4). Confidence interval of the odds ratio (Table 4). Whether crystalloid infusion rate was used as a numerical/categorical variable and whether it was the latter, whether dummy variables were used and different nominal variables for the extremes were used. Fig. 1: A parabolic relationship was found between crystalloid infusion volume and complications. Reprinted from Zhong T, Neinstein R, Massey C, et al. Intravenous fluid infusion rate in microsurgical breast reconstruction: Important lessons learned from 354 free flaps. Plast Reconstr Surg. 2011;128:1153–1160.Table 4: Multivariate Analysis of Factors Associated with Postoperative ComplicationsThe crystalloid infusion rate was used as a continuous variable in the units of milliliters per kilogram per 24 hours. Below are the formulas used for calculating the estimated probability of complication based on crystalloid and time under anesthesia (–2.8550 is the intercept from the logistic model). It is important to note that the model is based on a data set with only 54 events, so it is not recommended that someone use it to try to predict the likelihood of a complication, as there would be a lot of uncertainty surrounding these probabilities. Toni Zhong, F.R.S.C.(C.), M.H.S. Stefan O. P. Hofer, M.D., Ph.D. University Health Network, Toronto, Ontario, Canada DISCLOSURE The authors have no financial interest to declare in relation to the content of this communication.

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)0.002
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.059
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0120.014
Insufficient payload (model declined to judge)0.0590.035

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 head0.022
GPT teacher head0.255
Teacher spread0.233 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2012
Admission routes2
Has abstractyes

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