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Patient-reported outcomes predicting non-fatal self-injury after a cancer diagnosis: A population-based case-control study.

2021· article· en· W3168343092 on OpenAlexaffabout
Julie Hallet, Rinku Sutradhar, Elie Isenberg‐Grzeda, Christopher W. Noel, Alyson Mahar, Simone N. Vigod, James M. Bolton, Julie M. Deleemans, Wing‐Lok Chan, Victoria Zuk, Natalie G. Coburn, Antoine Eskander

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of CalgaryUniversity of TorontoUniversity Health NetworkSunnybrook HospitalHealth Sciences CentrePrincess Margaret Cancer CentreUniversity of ManitobaSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineConditional logistic regressionCancerLogistic regressionDistressPopulationInternal medicinePediatricsCase-control studyClinical psychologyEnvironmental health

Abstract

fetched live from OpenAlex

e18583 Background: Psychological distress is key in cancer care. Non-fatal self-injury (NFSI) represents a patient-centred, objectively measured manifestation of the greatest level of distress occurring in 3 out of 1,000 patients after a new cancer diagnosis. How to identify patients at high risk for NFSI remains unknown. We examined the relationships between routinely collected patient-reported outcomes measures and the risk of subsequent NFSI following a cancer diagnosis. Methods: We conducted a population-based case-control study of patients with a new cancer diagnosis (2007-2019) reporting a Edmonton Symptom Assessment (ESAS) score within 36 months of diagnosis. Cases were patients with NFSI within 36 months of diagnosis and controls those without NFSI (assigned a dummy index date corresponding to the case’s NFSI). Cases and controls were matched 1:4 on time from cancer diagnosis, ESAS record in 180 days prior index, age, sex, cancer site, and history of self-injury. Multivariable conditional logistic regression assessed the association between moderate-to-severe ESAS symptoms and total ESAS (t-ESAS) score with NFSI within 180 days. Results: Of 408,858 patients reporting >1 ESAS within 36 months of cancer diagnosis, 748 patients had a NFSI, including 425 patients with an ESAS score within 180 days preceding the NFSI event. Of those 406 cases were matched to 1,624 controls without NFSI. Cases reported a higher proportion of moderate-to-severe symptoms and higher t-ESAS than controls prior to the event (p < 0.01 for all). After adjusting for psychiatric illness history and cancer therapy received, reporting of moderate-to-severe anxiety (odds ratio – OR 1.61, 95%CI 1.14-2.27), depression (OR 1.66, 95%CI 1.2-2.31), and shortness of breath (OR 1.65, 955CI 1.18-2.31) were independently associated with higher risk of NFSI within 180 days. Each 10-point increase in t-ESAS (score 0-90) was independently associated with increased risk of NFSI within 180 days (OR 1.51; 95%CI 1.40-1.63). Conclusions: Reporting moderate-to-severe anxiety, depression, and shortness of breath, and increasing t-ESAS after cancer diagnosis are associated with higher odds of NFSI in the following 180 days. These data support the prospective identification of patients at high risk for NFSI via routine ESAS screening to improve supportive care. Patients with at-risk ESAS scores should receive tailored assessment, management, and longitudinal follow-up.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.045
GPT teacher head0.429
Teacher spread0.385 · 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
Published2021
Admission routes2
Has abstractyes

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