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Record W4294142491 · doi:10.3390/curroncol29090493

The Art of Counseling in the Treatment of Head and Neck Cancer: Exploratory Investigation among Perceptions of Health Professionals in Southern Italy

2022· article· en· W4294142491 on OpenAlexvenueno aff
Raffaele Addeo, Luca Pompella, Pasquale Vitale, Silvia Ileana Fattoruso, Ilaria Di Giovanni, Francesco Perri, Michele Caraglia, Morena Fasano, Raffaele Arigliani

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHead and neckMultidisciplinary approachHead and neck cancerFamily medicinePerceptionHealth professionalsQuality of life (healthcare)Exploratory researchNursingHealth careCancerSurgeryInternal medicinePsychology

Abstract

fetched live from OpenAlex

(1) Background: Recurrent and/or metastatic patients with head and neck squamous cell carcinoma show a poor prognosis, which has not changed significantly in 30 years. Preserving quality of life is a primary goal for this subset of patients; (2) Methods: A group of 19 physicians working in South Italy and daily involved in head and neck cancer care took an anonymous online survey aimed at revealing the level of knowledge and the application of communication techniques in daily patient care; (3) Results: Several specialists, 18 out 19 (95%), considered that patient participation in therapeutic choices is mandatory. The main obstacles to complete and reciprocate communication still consist of lack of time and staff, but also in the need for greater organization, which goes beyond the multidisciplinary strategy already used; (4) Conclusions: A greater impulse to training and updating on issues related to counseling can improve communication between the different clinicians involved in the treatment plan.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.383
GPT teacher head0.515
Teacher spread0.132 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations7
Published2022
Admission routes1
Has abstractyes

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