52 Developing and Calibrating a Sex-Specific Psychiatric Screener within the Post-Concussion Symptom Scale
Notice bibliographique
Résumé
Objective: Pre- and post-morbid mental health conditions can prolong recovery from concussion and are generally detrimental to athletic performance and quality of life. If psychiatric conditions can be identified in athletes at the time of baseline testing, psychological/psychiatric intervention can be implemented to prevent these complications. Given the time constraints on neuropsychological baseline testing, it is important to have time-efficient screening measures. As such, the purpose of this study was to develop and calibrate a psychiatric screening measure within the Post-Concussion Symptom Scale (PCSS) from the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT), which is commonly administered to athletes at baseline, thereby “killing two birds with one stone”: (1) screening for psychiatric conditions and (2) obtaining a baseline measurement of concussion-like symptoms. Participants and Methods: Participants were 278 undergraduate students from a Canadian university with a mean age of 21.87 years (SD=4.87, range=18 to 52) and a sex composition of 64% females (n=179, Age: M=21.29 years-old, SD=4.34, range: 18 to 52) and 36% males (n=179, Age: M=22.93 years-old, SD=5.57, range: 18 to 50). Participants were a convenience sample collected via online survey platform in exchange for bonus points toward courses through a participant pool system between January and July 2021. The psychiatric screener consisted of the affective subscale from the PCSS (irritability, sadness, feeling more emotional, nervousness) and the criterion measure was the Depression, Anxiety, and Stress Scales (DASS-42). Statistical analyses were conducted in R v.4.3 and included confirmatory factor analysis and receiver operating characteristic (ROC) curve analyses. Although a balance was sought between sensitivity and specificity, the former was prioritized given that this is intended as a screening measure. Males and females were analyzed separately as females tend to report more symptoms than males. Mild, moderate, and severe elevations were predicted for depression, anxiety, and stress, based on standard DASS cutoffs. Results: The CFA analyses revealed good fit for both the PCSS (CFI=.992; TLI=.991; RMSEA=.053; SRMR=.066) and DASS (CFI=.995; TLI=.995; RMSEA=.053; SRMR=.065) models. Cutoffs of >3, >4, and >8 (SENS= .77-.80, SPEC= .52-.83) optimally classified males as having mild, moderate, and severe depression, respectively; and cutoffs of >8, >8, and >9 (SENS= .79-.83, SPEC= .63-.67) optimally classified females as having mild, moderate, and severe depression, respectively. A cutoff of >2 (SENS= .78-.81, SPEC= .35-.39) optimally classified males as having both mild and moderate anxiety (insufficient n in severe group); and >7, >8, and >9 (SENS= .80-.85, SPEC= .63-.68) optimally classified females as having mild, moderate, and severe anxiety. Cutoffs of >5and >8(SENS= .80-.86, SPEC= .70-.85) were optimal for detecting mild and moderate stress in males (insufficient n in severe group); and >8, >8, and >9 (SENS= .80.89, SPEC= .60-.75) were optimal in females. Conclusions: The affective subscale within the PCSS operates well as a psychiatric screening measure. In general, females had higher cutoffs and the cutoffs for mild and moderate levels of the conditions tended to be similar. Males were less onsistent, with cutoffs varying widely depending on the construct and severity.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».