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Record W4385265857 · doi:10.2196/49466

Surgical Excision Margins in Primary Care and Plastic Surgery for Keratinocytic Cancers Diagnosed via Teledermatology: Retrospective Observational Cross-Sectional Study

2023· article· en· W4385265857 on OpenAlexvenueno aff
José-Pablo Tirado-Pérez, Amanda Oakley

Bibliographic record

VenueIproceedings · 2023
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRetrospective cohort studySkin cancerSurgical marginIncidence (geometry)TeledermatologyCross-sectional studySurgeryCancerGeneral surgeryPathologyInternal medicineHealth careTelemedicine

Abstract

fetched live from OpenAlex

Background The incidence of keratinocytic cancers is increasing. In New Zealand, surgical treatment of skin cancers is often undertaken in primary care. In the Waikato district, general practitioners (GPs) are encouraged to confirm diagnoses via teledermatology. Histological examination should confirm clear surgical margins to reduce tumor recurrence. International guidelines recommend a lateral margin of ≥3 mm for basal cell carcinomas (BCCs) and ≥4 mm for squamous cell carcinomas (SCCs). Objective This study aimed to determine lateral and deep margins in keratinocytic cancer excisions performed by GPs (in a private setting) and plastic surgeons (in a private or public setting) after a teledermatologist had confirmed that excision was necessary. Demographic, clinical, and histological features were recorded. Methods A retrospective observational cross-sectional study was conducted. The sample in the electronic dermatology referral database included keratinocyte cancers recommended for excision from March to May 2022. Results Histological reports revealed that excision was complete in 186 of 201 confirmed cases of keratinocyte cancer. The lateral margins of resection were considered in 10 tumors and deep margins in 8 tumors. All incomplete excisions were carried out by GPs, 11 of which were on the head and neck. There were 133 BCCs, 100 of which were excised by a GP, 3 by a private plastic surgeon, and 30 by a public hospital surgeon. In total, 52 BCCs were present on the head and neck (25 excised by GPs, 25 by hospital plastic surgeons, and 2 by private plastic surgeons) and 81 were present on other sites (75 excised by GPs, 5 by hospital plastic surgeons, and 1 by a private plastic surgeon). Lateral margins were considered in 9 cases (of which 5 cases involved head and neck tumors). The minimum distance from the tumor to the lateral margin was <3 mm in 80 tumors: 64 were excised by a GP, 2 by private plastic surgeons, and 14 by hospital plastic surgeons. This distance was ≥3 mm in 44 tumors (27 excised by GPs, 1 by a private plastic surgeon, and 16 by hospital plastic surgeons). These data show significant adherence to surgical margin recommendations among plastic surgeons compared to that among GPs (odds ratio 2.873, CI 1.274-6.477; P=.009). There were 68 SCCs: 57 were excised by a GP, 2 by a private plastic surgeon, and 9 by a public hospital surgeon. In total, 21 SCCs were on the head and neck (14 excised by GPs, 6 by hospital plastic surgeons, and 1 by a private plastic surgeon) and 47 were on other sites (43 excised by GPs, 3 by hospital plastic surgeons, and 1 by a private plastic surgeon). Lateral margins were considered in 1 head and neck SCC case and were not reported in others. The minimum distance from the tumor to the lateral margin was <4 mm in 35 cases: 31 were excised by a GP, 1 by a private plastic surgeon, and 3 by a hospital plastic surgeon. This distance was ≥4 mm in 31 cases (24 excised by GPs, 1 by a private plastic surgeon, and 6 by hospital plastic surgeons). These data do not show significant difference in adherence to surgical margin recommendations between GPs and plastic surgeons (P>.05). Conclusions Complete resection reduces the risk of recurrence of keratinocytic tumors. GPs in our study were less likely than specialist surgeons to respect surgical margin recommendations established in international guidelines for managing keratinocytic cancer. Conflicts of Interest None declared.

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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 score0.003
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.049
GPT teacher head0.328
Teacher spread0.279 · 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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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