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Feasibility Assessment of Using the Complete Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) Item Library

2019· article· en· W2914157560 on OpenAlex
Daniel Shepshelovich, Kate McDonald, Anna Spreafico, Albiruni R. Abdul Razak, Philippe L. Bédard, Lillian L. Siu, Lori M. Minasian, Aaron R. Hansen

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

VenueThe Oncologist · 2019
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineCommon Terminology Criteria for Adverse EventsAdverse effectTerminologyMEDLINEClinical trialFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

The patient-reported outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) complements capture of symptomatic adverse events (AEs) by clinicians. Previous trials have typically used a limited subset of relevant symptomatic AEs to reduce patient burden. We aimed to determine the feasibility of administering all 80 AEs included in the PRO-CTCAE library by approaching consecutive patients enrolled in a large academic phase I program at three points in time. Here, we report a preplanned analysis after enrolling the first 20 patients. All items were answered on 51 of 56 potential visits (adherence 91%). Three (5%) additional PRO-CTCAE assessments were partially completed, and two (4%) were missed because of conflicting appointments. No patient withdrew consent or chose not to complete the assessments once enrolled on study. Future trials of experimental drugs that incorporate the PRO-CTCAE should consider using this unselected approach to identify adverse events more completely.

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)0.001
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 score0.017
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.214
GPT teacher head0.472
Teacher spread0.258 · 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