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Record W2307207023 · doi:10.1177/1534734615627721

The Point Prevalence of Malignancy in a Wound Clinic

2016· article· en· W2307207023 on OpenAlexaff
Farhad Ghasemi, Niloofar Anooshirvani, R. Gary Sibbald, Afsáneh Alavi

Bibliographic record

VenueThe International Journal of Lower Extremity Wounds · 2016
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsMedicineMalignancyBiopsyChronic woundSurgeryRetrospective cohort studyOdds ratioMalignant transformationDermatologyWound healingInternal medicinePathology

Abstract

fetched live from OpenAlex

The aim of this study was to determine the prevalence of malignant leg ulcers and to identify the most frequent characteristics of such wounds. This study was a retrospective investigation of patients with chronic leg ulcers in a North American tertiary wound clinic. Between January 2011 and September 2013, a total of 1189 patients with lower extremity wounds, including 726 patients with leg wounds, were identified. A total of 124 of the 726 had undergone a biopsy of their atypical wound, 16.1% (20/124) of which were malignant. Patients with malignant wounds were older than patients with nonmalignant leg wounds (P < .0001), and the common location of the malignant wound was the anterior shin (odds ratio = 3.5). The limitation of this analysis is the lack of distinction between malignant transformation of wounds and de novo presentation of malignancies as chronic nonhealing wounds. Three distinguishing morphological features in malignant wounds were irregular borders (P = .0002), presence of hypergranulation tissue (P < .0001), and friable/bleeding wound surface (P < .0001). The frequency of malignant wounds in patients with chronic leg ulcers highlights the need for a systematic approach, which would involve biopsy of wounds to identify malignancy in this patient population early on.

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)0.001
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 score0.058
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.028
GPT teacher head0.328
Teacher spread0.300 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

Explore more

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