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Pain Specialists' Evaluation of Patient's Prognosis During the First Visit Predicts Subsequent Depression and the Affective Dimension of Pain

2010· article· en· W2085698204 on OpenAlexaboutno aff
Zvia Rudich, Sheera F. Lerman, Golan Shahar

Bibliographic record

VenuePain Medicine · 2010
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsnot available
FundersIsrael Science Foundation
KeywordsDepression (economics)MedicineDimension (graph theory)Pain medicinePhysical therapyPsychiatryAnesthesiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the predictive value of physician's prognosis after patient's first visit to a pain specialty clinic. DESIGN: This is a prospective-longitudinal study in which patients completed questionnaires regarding their pain and psychological constructs before their first visit to a pain specialist and again after an average of 5 months. Physicians rated patient's prognosis immediately after the first visit. SETTING: This study was conducted at the outpatient specialty pain clinic at Soroka University Medical Center. PATIENTS: Forty-five chronic pain patients suffering from a range of nonmalignant pain conditions. OUTCOME MEASURES: Sensory and affective pain measured by the Short-Form McGill Pain Questionnaire and depressive symptoms measured by the Center for Epidemiological Studies-Depression Scale. RESULTS: Multiple regression analysis revealed that physician's rating of patient prognosis at Time 1 uniquely predicted subsequent depressive symptoms and affective pain but not sensory pain at Time 2 even after controlling for Time 1 levels of these variables. CONCLUSION: Physician's pessimistic evaluation of patient's prognosis after the first visit was longitudinally associated with an increase in depression and in the affective dimension of pain over time, but not with changes in the sensory component of pain. Referring to physician pessimism as a marker for pre-depressed patient may lead to early preventive interventions.

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)0.026
metaresearch head score (Gemma)0.004
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 score0.378
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.275
Teacher spread0.265 · 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

Citations17
Published2010
Admission routes1
Has abstractyes

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