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Physician Perceptions and Preferences in the Treatment of Acquired Immunodeficiency Syndrome (AIDS)-Related Lymphoma (ARL).

2006· article· en· W2468570663 on OpenAlexaffabout
Matthew C. Cheung, Mona Loutfy, Heather A. Leitch, Rena Buckstein, Kevin Imrie

Bibliographic record

VenueBlood · 2006
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsSt. Paul's HospitalMaple Leaf Medical ClinicUniversity of British ColumbiaHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineFamily medicineRegimenInternal medicine

Abstract

fetched live from OpenAlex

Abstract The treatment of ARL is complicated by the numerous immuno-chemotherapy, antiretroviral, and prophylactic options available to lymphoma specialists. The optimal management in the era of combination antiretroviral therapy (cART) is unclear. We administered a survey instrument to determine physician preferences and perceptions in the management of ARL and to assess the variability in treatment in Canada. The survey was developed with items grouped into key domains of ARL management (physician demographics, attitudes, and treatment preferences) and piloted for content validity and clarity. The final questionnaire was administered to lymphoma physicians with valid contact information in the provinces of Ontario (ON; n=155) and British Columbia (BC; n=48). The Dillman Tailored Design Method was followed for multi-modality (internet and standard mail) survey administration. Of 196 physicians, 131 either responded by completing the questionnaire (n=117; 60% response rate) or declining to participate (n=14; 7%). Most responders were male (63%), white (70%), practicing in an academic setting (63%), and belonged to a median age group range of 41–50 years. The majority (98%) had a positive attitude towards the treatment of ARL, as measured by a previously validated 2-item attitude scale. However, barriers to adequate care were still identified; 84% of physicians agreed that uncontrolled human immunodeficiency virus infection represented a major barrier to ARL care and 54% agreed that a patient’s concurrent intravenous drug abuse impaired care. Most physicians recommended the concomitant use of cART in the care of their patients with ARL (n=72 of 109 responses; 66%). Similarly, a majority of respondents recommended CHOP-like regimens (cyclophosphamide, doxorubicin, vincristine, and prednisone; n=92 of 108 responses; 85%) to form the backbone of chemotherapy. The addition of the rituximab was preferred by 41% physicians but not by 40% others, with remaining respondents unsure of the agent’s role. In logistic regression analysis, use of rituximab was predicted only by location of practice (province), after adjusting for other potential predictors including physician age, race, gender, practice environment (academic vs. community), years of experience, ARL patient volume, and modality of survey response. Physicians from BC were much more likely to administer rituximab than ON practitioners (OR 44.5; 95% CI: 7.76–255.0, p<0.0001). We explored the reasons that physicians did not recommend rituximab; most cited a concern for additional toxicity with rituximab in ARL. Eight physicians in ON highlighted the lack of formulary funding for rituximab in the province as the primary reason they did not treat patients with this agent. In the current cART era, Canadian physicians have a positive attitude towards ARL treatment. The majority prefer to use cART in combination with CHOP for AIDS-related diffuse large B-cell lymphoma. The use and perceived benefit of rituximab may be influenced by inter-provincial formulary differences and by regional variation in policy and guideline recommendations.

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)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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 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

Citations0
Published2006
Admission routes2
Has abstractyes

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