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Record W3096033062 · doi:10.3747/co.27.6547

Delay in Diagnosis of Patients with Head-and-Neck Cancer in Canada: Impact of Patient and Provider Delay

2020· article· en· W3096033062 on OpenAlexaffvenueabout
Shayan Kassirian, Agnieszka Dzioba, Stephanie A. Hamel, Krupal Patel, Axel Sahovaler, David A. Palma, Nancy Read, V. Venkatesan, Anthony C. Nichols, John Yoo, Kevin Fung, Adrian Mendez, S. Danielle MacNeil

Bibliographic record

VenueCurrent Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineReferralPresentation (obstetrics)Head and neck cancerCancerPediatricsFamily medicineHead and neckMultidisciplinary approachHealth careSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Background: Head-and-neck cancers (hncs) often present at an advanced stage, leading to poor outcomes. Late presentation might be attributable to patient delays (reluctance to seek treatment, for instance) or provider delays (misdiagnosis, prolonged wait time for consultation, for example). The objective of the present study was to examine the length and cause of such delays in a Canadian universal health care setting. Methods: Patients presenting for the first time to the hnc multidisciplinary team (mdt) with a biopsy-proven hnc were recruited to this study. Patients completed a survey querying initial symptom presentation, their previous medical appointments, and length of time between appointments. Clinical and demographic data were collected for all patients. Results: The average time for patients to have their first appointment at the mdt clinic was 15.1 months, consisting of 3.9 months for patients to see a health care provider (hcp) for the first time since symptom onset and 10.7 months from first hcp appointment to the mdt clinic. Patients saw an average of 3 hcps before the mdt clinic visit (range: 1-7). No significant differences in time to presentation were found based on stage at presentation or anatomic site. Conclusions: At our tertiary care cancer centre, a patient's clinical pathway to being seen at the mdt clinic shows significant delays, particularly in the time from the first hcp visit to mdt referral. Possible methods to mitigate delay include education about hnc for patients and providers alike, and a more streamlined referral system.

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)0.000
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 score0.449
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.048
GPT teacher head0.362
Teacher spread0.314 · 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

Citations35
Published2020
Admission routes3
Has abstractyes

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