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Record W2140507579 · doi:10.1200/jco.2012.41.9770

Follow-Up of Breast Cancer Survivors

2012· letter· en· W2140507579 on OpenAlexaff
Ellen Warner

Bibliographic record

VenueJournal of Clinical Oncology · 2012
Typeletter
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineWorryBreast cancerSurvivorship curveCancerOdds ratioQuality of life (healthcare)Confidence intervalInternal medicineFamily medicineOncologyPatient satisfactionNursingPsychiatryAnxiety

Abstract

fetched live from OpenAlex

important study of the follow-up preferences of breast cancer survivors. However, I was struck by the marked disconnect between the study’s findings and the authors’ conclusions. In this survey of the self-perceived impact of follow-up by different types of providers, the 218 breast cancer survivors who completed the questionnaire overwhelmingly preferred follow-up with a medical oncologist to follow-up with a primary care physician (PCP) or nurse practitioner (NP). The odds ratios for the belief that medical oncologist follow-up relative to PCP follow-up would decrease breast cancer worry, and increase the chance of surviving cancer, were 36 (95% CI, 21 to 62) and 42 (95% CI, 24 to 74), respectively. Confidence in follow-up by an NP was nonsignificantly lower than that for a PCP. Nevertheless, the authors concluded that “Breast cancer survivors are comfortable with both PCPs and NPs providing follow-up care . . . the NP-led survivorship clinic model, with increased guidance for PCPs, offers a promising route for improving quality of and satisfaction with survivor care.” 1(p158) Breast cancer worry and fear of recurrence are high among breast cancer survivors, and often correlate poorly with objective prognosis. For example, in one study of 136 patients treated with curative intent, more than 50% had moderate to high levels of fear of recurrence. 2 It is inconceivable that a follow-up model that would exacerbate such fears would improve patient satisfaction. Shifting follow-up care from the oncologist to a PCP or NP could have more subtle detrimental effects on patient care, particularly for future medical oncology graduates, who would see far fewer follow-up patients during their training, and would never acquire the experience of monitoring large numbers of patients treated with adjuvant systemic therapy over several years. How will they be able to have balanced discussions with patients for whom the indication for chemotherapy is borderline if they have never seen the 40-year-old ICU nurse who can no longer work because of “chemo brain,” or the mother of six children who develops a cardiomyopathyrelated stroke? Nor will their perspective be balanced if they are asked to see only the patients with complications in the NP-led follow-up clinic next door. Even if patient care could somehow be unaffected, medical oncologists would have much to lose in terms of job satisfaction if we handed most, if not all, of our patients over to other health care professionals for follow-up. Undoubtedly most of us would prefer to see another patient responding to neoadjuvant chemotherapy than a whining but otherwise well survivor. But, more important, a clinic with several women in their thirties dying of metastatic disease is only bearable if, in the same clinic, there is also a patient, expected to have died 15 years ago of locally advanced disease, proudly showing pictures of her son’s wedding or daughter’s graduation. Referring patients with breast cancer who have completed active treatment to their PCP or a NP for follow-up is appropriate and optimal in many cases. Most patients, who greatly prefer medical oncologist follow-up initially, will gladly “graduate” back to their PCP after several years. However, the decision and timing should be made jointly by the patient and medical oncologist, taking into account factors such as patient preference and tumor prognosis. A health care system that allows both traditional and alternative follow-up models will maximize patient satisfaction, maintain the quality of oncology care, and avoid oncologist burn-out.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.116
GPT teacher head0.448
Teacher spread0.332 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2012
Admission routes1
Has abstractyes

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