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Record W2433128876 · doi:10.1093/neuonc/now081.22

QOS-22IS A SEPARATE AFTERCARE CLINIC FOR PEDIATRIC CNS TUMOR PATIENTS NECESSARY?

2016· article· en· W2433128876 on OpenAlexaff
Katrin Scheinemann, JoAnn Duckworth, Sheila K. Singh

Bibliographic record

VenueNeuro-Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicTumors and Oncological Cases
Canadian institutionsMcMaster Children's Hospital
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Over the past decades more and more patients diagnosed with a CNS tumor in childhood are long term survivors. Multiple publications have shown that long term morbidity is the highest in this disease group. METHODS: From the database of the primary nurse/ case manager data of all patients followed in the neurooncology aftercare clinic were collected. RESULTS: Over the past years around 120 patients with 260 visits were followed in this clinic on a yearly basis. PatienT's age was from 5 to 50 years, visit frequency from 4 times to once a year. The clinic was held once a week in the afternoon with 4 to 6 patients per clinic. At the start of each clinic the most recent imaging was reviewed by the pediatric neuroradiologist and the ongoing problems and clinical findings were discussed. Patients were initially seen by the most relevant discipline (neurosurgery, neurooncology or radiation oncology) and then seen by the whole group. All were also seen by the rehabilitation team (occupational therapy, physiotherapy and dietician) as well as by the social worker and the neuropsychologist. If other consults like ophthalmology or imaging are scheduled for the visit, they are organized for the morning. CONCLUSION: From our experience a separate aftercare clinic for pediatric CNS tumor patients is necessary given the multiple morbidities and issues. Patients and parents appreciate this aftercare model with a multidisciplinary team and multiple appointments in one day to address all their issues.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.001

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 head0.038
GPT teacher head0.342
Teacher spread0.304 · 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 teacher head, 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
Published2016
Admission routes1
Has abstractyes

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