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Record W2793125759 · doi:10.1097/ipc.0000000000000597

All in The Name of Vanity

2018· article· en· W2793125759 on OpenAlexaff
Yan Li, Linda M. Spooner, George M. Abraham

Bibliographic record

VenueInfectious Diseases in Clinical Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsSaint-Vincent Hospital
Fundersnot available
KeywordsMedicineAzithromycinAmikacinRegimenCefoxitinMycobacterium abscessusSurgeryImipenemRefractory (planetary science)AntibioticsAdverse effectInternal medicineMycobacteriumAntibiotic resistance

Abstract

fetched live from OpenAlex

Abstract We report a case of refractory skin abscesses caused by Mycobacterium abscessus resulting from cosmetic surgery. A 25-year-old woman developed recurrent cutaneous infection caused by M. abscessus after multiple cosmetic surgeries in the Dominican Republic. Despite of the removal of infected implants, she failed a 20-month treatment with azithromycin, cefoxitin, and amikacin. The patient presented to our institution 28 months after the initial surgeries and approximately 5 months after initiation of her latest antimicrobial regimen of azithromycin, imipenem, and amikacin. Here, she was restarted on cefoxitin 2 g daily, amikacin 750 mg daily, and azithromycin 1000 mg daily. She completed an additional 4 months (a total of 9 months) of therapy with complete clinical resolution. In conclusion, clinical suspicion of M. abscessus infection is essential for the diagnosis. Patient compliance is integral to a successful outcome because the treatment involves prolonged duration, continuous intravenous antibiotics, and multiple potential adverse effects.

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)0.001
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.127
Threshold uncertainty score0.425

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1270.038

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 head0.100
GPT teacher head0.497
Teacher spread0.397 · 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
Published2018
Admission routes1
Has abstractyes

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