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Record W2539118526 · doi:10.1097/der.0000000000000236

Metal Hypersensitivity and Orthopedic Implants: Survey of Orthopedic Surgeons

2016· article· en· W2539118526 on OpenAlexvenueno aff
Katherine K. Hallock, Natalie H. Vaughn, Paul J. Juliano, James G. Marks

Bibliographic record

VenueDermatitis · 2016
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOrthopedic surgeryPatch testingDentistryFamily medicinePhysical therapySurgeryDermatologyContact dermatitisAllergy

Abstract

fetched live from OpenAlex

BACKGROUND: There is no clear consensus among orthopedic surgeons concerning metal hypersensitivity screening and orthopedic implants. OBJECTIVE: This study investigated practices and opinions about metal hypersensitivity and orthopedic implants via a survey administered to practicing orthopedists. METHODS: A questionnaire was sent to members of the Pennsylvania Orthopaedic Society electronically. Respondents were asked about preoperative and postoperative screening habits concerning metal hypersensitivity and implants. RESULTS: Forty-four physicians completed the survey. Only 11% of respondents reported that they always or often screen patients for metal hypersensitivity. Fifty percent of respondents stated that they only rarely refer patients for patch testing (and the remainder never do). If, however, patients were found to have a positive patch test, most providers were very likely to use a different implant. Other respondents were skeptical of the relationship between metal hypersensitivity and implant failure. Dermatitis, pain, and loosening were common reasons for postoperative testing. Seventy percent of respondents said that patch testing rarely or never changed their decision making. CONCLUSIONS: This study is reflective of the lack of consensus between orthopedists regarding patch testing. It demonstrates the diversity of opinions among orthopedists, the need for additional dialogue between orthopedic and dermatology specialties, and the need for larger studies investigating outcomes and metal hypersensitivity.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.022
GPT teacher head0.252
Teacher spread0.231 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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