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Record W2981749757 · doi:10.1177/2292550319880911

Assessment of the Readability, Adequacy, and Suitability of Online Patient Education Resources for Benign Vascular Tumours Using the DISCERN Instrument

2019· article· en· W2981749757 on OpenAlexaff
Minh Huynh, Katie Hicks, Claudia Malic

Bibliographic record

VenuePlastic Surgery · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsReadabilityMEDLINEThe InternetMedicineReading (process)HumanitiesPsychologyMedical educationComputer scienceWorld Wide WebPolitical scienceArt

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aims to assess the quality and readability of Internet-based patient resources for vascular tumours in order to understand which areas require improvement. METHODS: A World Wide Web search was performed, in addition to a literature review using PubMed, Ovid MEDLINE, and EMBASE. Any material that contained information on vascular tumours pertaining to patient education was included. We evaluated resources with DISCERN and Flesch Reading Ease scores when applicable. The language of publication was restricted to English and French. This review was registered with PROSPERO (CRD42018087885). RESULTS: A total of 117 online resources were screened, with 73 resources included in the final analysis. The overall DISCERN rating for the patient resources was 1.8 (0.8). The majority of online resources failed to depict the entire spectrum of benign vascular tumours. The mean Flesch score was 36 (19), which translates to a college-level readability. CONCLUSION: The majority of resources were not adequate or comprehensive and were written at a much higher level than the average reader would be expected to comprehend.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.014
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
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 head0.052
GPT teacher head0.399
Teacher spread0.346 · 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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