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Potential Complications of Intralesional Laser Photocoagulation for Extensive Vascular Malformations

2001· article· en· W1965560265 on OpenAlexaff
David M. Fisher, Cheng‐Jen Chang, Jun-Jin Chua, Yu-Ray Chen, B M Achauer

Bibliographic record

VenueAnnals of Plastic Surgery · 2001
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations and Hemangiomas
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineButtocksSurgeryThrombocytosisLeukocytosisLaser therapyHemangiomaDermatologyLaserInternal medicinePlatelet

Abstract

fetched live from OpenAlex

Vascular anomalies remain a challenge for both patients and plastic surgeons. Recently, promising results have been reported using intralesional photocoagulation (ILP) to treat extensive vascular lesions. At the authors' center, they have treated more than 300 patients with vascular anomalies in different parts of the body between 1996 and 1999. They describe their operative techniques of ILP. Laser pulses of a 1,064-nm wavelength from the Nd:YAG laser were delivered to the target tissues with a 600-microm optical fiber. They report 2 patients who developed complications after a single session of ILP therapy for their extensive vascular malformations. The first patient had Klippel-Trenaunay syndrome (capillary-lymphaticovenous malformations) with widespread involvement of her buttocks and left lower limb. She had severe leukocytosis, thrombocytosis, and hyperkalemia that resolved with intravenous hydration, antibiotics, and sodium bicarbonate. In their second patient, the entire left upper limb was affected. Her total red cell count diminished by a quarter and her hemoglobin concentration dropped by more than 3 g%. This was corrected gradually with supplemental oral hematinics. Although these complications resolved uneventfully in their patients, they hope that their possible development will caution anyone who may wish to attempt this new method of therapy.

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)0.001
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 score0.472
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.071
GPT teacher head0.318
Teacher spread0.247 · 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

Citations16
Published2001
Admission routes1
Has abstractyes

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