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Record W2151593637 · doi:10.1177/1078155211434852

The oncology pharmacy in cancer care delivery in a resource-constrained setting in western Kenya

2012· article· en· W2151593637 on OpenAlex
Robert Matthew Strother, Kamakshi V. Rao, Kelly M. Gregory, Beatrice Jakait, Naftali Busakhala, Ellen Schellhase, Sonak Pastakia, Monika K. Krzyzanowska, Patrick J. Loehrer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Oncology Pharmacy Practice · 2012
Typearticle
Languageen
FieldHealth Professions
TopicSafe Handling of Antineoplastic Drugs
Canadian institutionsUniversity of Toronto
FundersEli Lilly and Company
KeywordsMedicinePharmacyMultidisciplinary approachHealth careCancerResource (disambiguation)OncologyIntensive care medicineFamily medicineNursingInternal medicineEconomic growth

Abstract

fetched live from OpenAlex

The movement to deliver cancer care in resource-limited settings is gaining momentum, with particular emphasis on the creation of cost-effective, rational algorithms utilizing affordable chemotherapeutics to treat curable disease. The delivery of cancer care in resource-replete settings is a concerted effort by a team of multidisciplinary care providers. The oncology pharmacy, which is now considered integral to cancer care in resourced medical practice, developed over the last several decades in an effort to limit healthcare provider exposure to workplace hazards and to limit risk to patients. In developing cancer care services in resource-constrained settings, creation of oncology pharmacies can help to both mitigate the risks to practitioners and patients, and also limit the costs of cancer care and the environmental impact of chemotherapeutics. This article describes the experience and lessons learned in establishing a chemotherapy pharmacy in western Kenya.

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.

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)0.011
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.005
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 head0.098
GPT teacher head0.522
Teacher spread0.424 · 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