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Record W4372349157 · doi:10.1101/2023.05.05.23289575

Counting your chickens before they hatch: improvements in an untreated chronic pain population, beyond regression to the mean and the placebo effect

2023· preprint· en· W4372349157 on OpenAlexaff
Monica Sean, Alexia Coulombe-Lévêque, William Nadeau, Anne-Catherine Charest, Marylie Martel, Guillaume Léonard, Pascal Tétreault

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsChronic painPhysical therapyPlaceboPopulationMedicineObservational studyLow back painPain catastrophizingAnxietyRegression toward the meanInternal medicinePsychiatryStatistics

Abstract

fetched live from OpenAlex

Abstract Background and aims Isolating the effect of an intervention from the natural course and fluctuations of a condition is a challenge in any clinical trial, particularly in the field of pain. Regression to the mean (RTM), wherein extreme scores are more likely to be followed by more average scores, may explain some of those observed fluctuations. However, while this phenomenon is relatively well-known, its effect on outcome measures is rarely quantified, and often only evoked as a potential confound. In this paper, we describe and quantify such symptom fluctuations in a chronic pain population in the absence of treatment, and compare the relative stability of various self-reported outcome measures in untreated chronic low back pain (CLBP) patients and healthy controls (HC). Methods Twenty-three untreated CLBP patients and 25 HC took part in this observational study, wherein they were asked to complete an array of commonly used questionnaires in pain studies (including the Pain Catastrophizing Scale [PCS], State and Trait Anxiety Inventory [STAI], Central Sensitization Inventory [CSI], Pain Disability Index [PDI], Brief Pain Inventory [BPI] etc.) during each of three visits (V1, V2, V3) at 2-month intervals. Scores at V1 were classified into three subgroups (extremely high, normal and extremely low), based on z-scores. The average delta (Δ=V2-V1) was calculated for each subgroup, for each questionnaire, to describe the evolution of scores over time. This analysis was repeated with the data for V2 and V3. Results High initial scores were likely to be followed by more average scores; for instance, the “extremely high” subgroup for the PCS (a reputably ‘stable’ questionnaire) had an average decrease of 12/52 from V1 to V2. Participants with “average” initial scores tended to show a small decrease over time, and participants with “extremely low” initial scores tended to remain stable. However, while the pattern of fluctuation in the three subgroups was similar across questionnaires, the magnitude of these fluctuations varied greatly. The STAI and CSI were the most stable questionnaires of all, with even the “extremely high” subgroup showing little or no improvement over time. The least stable questionnaires were the PCS, PDI and BPI. Discussion and conclusion These pain trajectories in untreated patients cannot be attributable to RTM alone because of their asymmetry, nor to the placebo effect as they occurred in the absence of any intervention. We propose that the observed improvements could be the result of an Effect of Care, wherein participants had meaningful improvements simply from taking part in a study and talking about their pain to benevolent research staff, despite the absence of active or sham treatment. These findings have important clinical ramifications. Beyond simply raising a flag as to the existence (and significance) of Effect of Care, we provide a questionnaire-specific baseline of expected fluctuations based on initial score, against which researchers can compare results from clinical trials when trying to isolate the effect of their intervention.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.015
GPT teacher head0.303
Teacher spread0.288 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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