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Record W2910634035 · doi:10.7759/cureus.3877

Poor Readability of Online Patient Resources Regarding Sentinel Lymph Node Biopsy for Melanoma

2019· article· en· W2910634035 on OpenAlexaff
Paul P Yen, Sam M. Wiseman

Bibliographic record

VenueCureus · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReadabilityMedicineSentinel lymph nodeReading (process)BiopsyMelanomaGrade levelMedical physicsRadiologyCancerComputer scienceMathematics educationInternal medicine

Abstract

fetched live from OpenAlex

Background Established guidelines recommend that patient educational materials should be set at no higher than a sixth-grade reading level to be considered adequately comprehensible to the general public. Our study objective was to assess the readability of online patient resources related to sentinel lymph node biopsy (SLNB) as part of treatment for melanoma. Materials and methods The top 50 results from a Google search (search terms: "sentinel lymph node biopsy melanoma") were analyzed using seven established readability formulae in order to determine their level of adherence to current guidelines. Results We found that the readability of available online patient resources is currently very poor, with only 12% of the websites meeting the sixth-grade reading level criteria according to at least one measure, and 0% meeting the criteria according to all seven assessment tools. Furthermore, half of search results were peer-reviewed academic journal articles not intended for the general public. Discussion and conclusions Online patient resources related to SLNB carried out as part of melanoma treatment have poor readability. Several simple measures may be taken in order to make these resources more accessible and comprehensible to a broader audience. These resources should undergo ongoing evaluation, with the ultimate goal being improved readability and patient education.

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)0.001
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 score0.185
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.043
GPT teacher head0.397
Teacher spread0.354 · 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
Published2019
Admission routes1
Has abstractyes

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